Tag Archives: datasharing

Data Protection Bill 2017: summary of source links

The Data Protection Bill [Exemptions from GDPR] was introduced to the House of Lords on 13 September 2017
  • Introduction page [link] 14.9.2017
  • Data Protection Bill (HL Bill 66) [web link]
  • Data Protection Bill (HL Bill 66) [PDF] 832KB, 218 pages
  • Progress of the Data Protection Bill [HL] 2017-19 [web link]
UK Data Protection Bill Overview
  • Data Protection Bill Explanatory Notes [PDF], 1.2MB, 112 pages
  • Data Protection Bill Overview Factsheet [PDF], 229KB, 4 pages
  • Data Protection Bill Impact Assessment [PDF], 123KB, 5 pages
The General Data Protection Regulation

The General Data Protection Regulation [PDF] 959KB, 88 pages

Related Factsheets
  • General Processing Factsheet, [PDF], 141KB, 3 pages
  • Law Enforcement Data Processing Factsheet [PDF], 226KB, 3 pages
  • National Security Data Processing Factsheet [PDF], 231KB, 4 pages
These parts of the bill concern the function of the Information Commissioner and her powers of enforcement
  • Information Commissioner and Enforcement Factsheet [PDF] 223KB, 4 pages
  • Data sharing code of practice [PDF]
GDPR possible derogations

Source credit Amberhawk: Chris Pounder

Member State law can allow modifications to Articles 4(7), 4(9),  6(2), 6(3)(b), 6(4),  8(1), 8(3), 9(2)(a), 9(2)(b), 9(2)(g), 9(2)(h), 9(2)(i), 9(2)(j), 9(3), 9(4),  10,  14(5)(b), 14(5)(c), 14(5)(d),  17(1)(e), 17(3)(b), 17(3)(d), 22(2)(b),  23(1)(e),  26(1),  28(3), 28(3)(a), 28(3)(g), 28(3)(h), 28(4),  29,  32(4),  35(10), 36(5),  37(4),  38(5),  49(1)(g), 49(4), 49(5),  53(1), 53(3),  54(1), 54(2),  58(1)(f), 58(2), 58(3), 58(4), 58(5),  59,  61(4)(b),  62(3),  80,  83(5)(d), 83(7), 83(8),  85,  86,  87,  88,  89,  and 90 of the GDPR.

Other relevant significant connected legislation
  • The Police and Crime Directive [web link] 
  • EU Charter of Fundamental Rights – European Commission [link]
  • The proposed Regulation on Privacy and Electronic Communications [web link]
  • Draft modernised convention for the protection of individuals with regard to the processing of personal data (convention 108)
Data Protection Bill Statement of Intent
  • DCMS Statement of Intent [PDF] 229KB, 4 pages
  • Letter to Stakeholders [PDF] 184KB, 2 pages 7 Aug 2017
Other links on derogations and data processing
  • On Adequacy: Data transfers between the EU and UK post Brexit? Andrew D. Murray Article [link]
  • Two Birds [web link]
  • ICO legal basis for processing and children [link]
  • Public authorities under the Freedom of Information Act (ICO) Public authorities under FOIA 120160901 Version: 2.2 [link] 
Blogs [links in date of post]
  • Amberhawk
    • On Adequacy:  Draconian powers in EU Withdrawal Bill can negate new Data Protection law [13.09]
    • Queen’s Speech, and the promised “Data Protection (Exemptions from GDPR) Bill [29.06]
  • Jon Baines
    • Serious DCMS error about consent data protection [11.08]
  • Eoin O’Dell
    • The UK’s Data Protection Bill 2017: repeals and compensation – updated: On DCMS legislating for Art 82 GDPR. [14.09]

Data Protection Bill Consultation: General Data Protection Regulation Call for Views on exemptions
  • New Data Protection Bill: Our planned reforms [PDF] 952KB, 30 pages
  • London Economics: Research and analysis to quantify benefits arising from personal data rights under the GDPR [PDF] 3.76MB 189 pages
  • ICO response to DCMS [link]
  • ESRC joint submissions on EU General Data Protection Regulation in the UK – Wellcome led multi org submission plus submission from British Academy / Erdos [link]
  • defenddigitalme response to the DCMS [link]
Minister for Digital Matt Hancock’s keynote address to the UK Internet Governance Forum, 13 September [link].

“…the Data Protection Bill, which will bring our data protection regime into the twenty first century, giving citizens more sovereignty over their data, and greater penalties for those who break the rules.

“With AI and machine learning, data use is moving fast. Good use of data isn’t just about complying with the regulations, it’s about the ethical use of data too.

“So good governance of data isn’t just about legislation – as important as that is – it’s also about establishing ethical norms and boundaries, as a society.  And this is something our Digital Charter will address too.”

Media links

14.09 BBC UK proposes exemptions to Data Protection Bill

Crouching Tiger Hidden Dragon: the making of an IoT trust mark

The Internet of Things (IoT) brings with it unique privacy and security concerns associated with smart technology and its use of data.

  • What would it mean for you to trust an Internet connected product or service and why would you not?
  • What has damaged consumer trust in products and services and why do sellers care?
  • What do we want to see different from today, and what is necessary to bring about that change?

These three pairs of questions implicitly underpinned the intense day of  discussion at the London Zoo last Friday.

The questions went unasked, and could have been voiced before we started, although were probably assumed to be self-evident:

  1. Why do you want one at all [define the problem]?
  2. What needs to change and why [define the future model]?
  3. How do you deliver that and for whom [set out the solution]?

If a group does not agree on the need and drivers for change, there will be no consensus on what that should look like, what the gap is to achieve it, and even less on making it happen.

So who do you want the trustmark to be for, why will anyone want it, and what will need to change to deliver the aims? No one wants a trustmark per se. Perhaps you want what values or promises it embodies to  demonstrate what you stand for, promote good practice, and generate consumer trust. To generate trust, you must be seen to be trustworthy. Will the principles deliver on those goals?

The Open IoT Certification Mark Principles, as a rough draft was the outcome of the day, and are available online.

Here’s my reflections, including what was missing on privacy, and the potential for it to be considered in future.

I’ve structured this first, assuming readers attended the event, at ca 1,000 words. Lists and bullet points. The background comes after that, for anyone interested to read a longer piece.

Many thanks upfront, to fellow participants, to the organisers Alexandra D-S and Usman Haque and the colleague who hosted at the London Zoo. And Usman’s Mum.  I hope there will be more constructive work to follow, and that there is space for civil society to play a supporting role and critical friend.


The mark didn’t aim to fix the IoT in a day, but deliver something better for product and service users, by those IoT companies and providers who want to sign up. Here is what I took away.

I learned three things

  1. A sense of privacy is not homogenous, even within people who like and care about privacy in theoretical and applied ways. (I very much look forward to reading suggestions promised by fellow participants, even if enforced personal openness and ‘watching the watchers’ may mean ‘privacy is theft‘.)
  2. Awareness of current data protection regulations needs improved in the field. For example, Subject Access Requests already apply to all data controllers, public and private. Few have read the GDPR, or the e-Privacy directive, despite importance for security measures in personal devices, relevant for IoT.
  3. I truly love working on this stuff, with people who care.

And it reaffirmed things I already knew

  1. Change is hard, no matter in what field.
  2. People working together towards a common goal is brilliant.
  3. Group collaboration can create some brilliantly sharp ideas. Group compromise can blunt them.
  4. Some men are particularly bad at talking over each other, never mind over the women in the conversation. Women notice more. (Note to self: When discussion is passionate, it’s hard to hold back in my own enthusiasm and not do the same myself. To fix.)
  5. The IoT context, and risks within it are not homogenous, but brings new risks and adverseries. The risks for manufacturers and consumers and the rest of the public are different, and cannot be easily solved with a one-size-fits-all solution. But we can try.

Concerns I came away with

  1. If the citizen / customer / individual is to benefit from the IoT trustmark, they must be put first, ahead of companies’ wants.
  2. If the IoT group controls both the design, assessment to adherence and the definition of success, how objective will it be?
  3. The group was not sufficiently diverse and as a result, reflects too little on the risks and impact of the lack of diversity in design and effect, and the implications of dataveillance .
  4. Critical minority thoughts although welcomed, were stripped out from crowdsourced first draft principles in compromise.
  5. More future thinking should be built-in to be robust over time.

IoT adversaries: via Twitter, unknown source

What was missing

There was too little discussion of privacy in perhaps the most important context of IoT – inter connectivity and new adversaries. It’s not only about *your* thing, but things that it speaks to, interacts with, of friends, passersby, the cityscape , and other individual and state actors interested in offense and defense. While we started to discuss it, we did not have the opportunity to discuss sufficiently at depth to be able to get any thinking into applying solutions in the principles.

One of the greatest risks that users face is the ubiquitous collection and storage of data about users that reveal detailed, inter-connected patterns of behaviour and our identity and not seeing how that is used by companies behind the scenes.

What we also missed discussing is not what we see as necessary today, but what we can foresee as necessary for the short term future, brainstorming and crowdsourcing horizon scanning for market needs and changing stakeholder wants.

Future thinking

Here’s the areas of future thinking that smart thinking on the IoT mark could consider.

  1. We are moving towards ever greater requirements to declare identity to use a product or service, to register and log in to use anything at all. How will that change trust in IoT devices?
  2. Single identity sign-on is becoming ever more imposed, and any attempts for multiple presentation of who I am by choice, and dependent on context, therefore restricted. [not all users want to use the same social media credentials for online shopping, with their child’s school app, and their weekend entertainment]
  3. Is this imposition what the public wants or what companies sell us as what customers want in the name of convenience? What I believe the public would really want is the choice to do neither.
  4. There is increasingly no private space or time, at places of work.
  5. Limitations on private space are encroaching in secret in all public city spaces. How will ‘handoffs’ affect privacy in the IoT?
  6. Public sector (connected) services are likely to need even more exacting standards than single home services.
  7. There is too little understanding of the social effects of this connectedness and knowledge created, embedded in design.
  8. What effects may there be on the perception of the IoT as a whole, if predictive data analysis and complex machine learning and AI hidden in black boxes becomes more commonplace and not every company wants to be or can be open-by-design?
  9. Ubiquitous collection and storage of data about users that reveal detailed, inter-connected patterns of behaviour and our identity needs greater commitments to disclosure. Where the hand-offs are to other devices, and whatever else is in the surrounding ecosystem, who has responsibility for communicating interaction through privacy notices, or defining legitimate interests, where the data joined up may be much more revealing than stand-alone data in each silo?
  10. Define with greater clarity the privacy threat models for different groups of stakeholders and address the principles for each.

What would better look like?

The draft privacy principles are a start, but they’re not yet aspirational as I would have hoped. Of course the principles will only be adopted if possible, practical and by those who choose to. But where is the differentiator from what everyone is required to do, and better than the bare minimum? How will you sell this to consumers as new? How would you like your child to be treated?

The wording in these 5 bullet points, is the first crowdsourced starting point.

  • The supplier of this product or service MUST be General Data Protection Regulation (GDPR) compliant.
  • This product SHALL NOT disclose data to third parties without my knowledge.
  • I SHOULD get full access to all the data collected about me.
  • I MAY operate this device without connecting to the internet.
  • My data SHALL NOT be used for profiling, marketing or advertising without transparent disclosure.

Yes other points that came under security address some of the crossover between privacy and surveillance risks, but there is as yet little substantial that is aspirational to make the IoT mark a real differentiator in terms of privacy. An opportunity remains.

It was that and how young people perceive privacy that I hoped to bring to the table. Because if manufacturers are serious about future success, they cannot ignore today’s children and how they feel. How you treat them today, will shape future purchasers and their purchasing, and there is evidence you are getting it wrong.

The timing is good in that it now also offers the opportunity to promote consistent understanding, and embed the language of GDPR and ePrivacy regulations into consistent and compatible language in policy and practice in the #IoTmark principles.

User rights I would like to see considered

These are some of the points I would think privacy by design would mean. This would better articulate GDPR Article 25 to consumers.

Data sovereignty is a good concept and I believe should be considered for inclusion in explanatory blurb before any agreed privacy principles.

  1. Goods should by ‘dumb* by default’ until the smart functionality is switched on. [*As our group chair/scribe called it]  I would describe this as, “off is the default setting out-of-the-box”.
  2. Privact by design. Deniability by default. i.e. not only after opt out, but a company should not access the personal or identifying purchase data of anyone who opts out of data collection about their product/service use during the set up process.
  3. The right to opt out of data collection at a later date while continuing to use services.
  4. A right to object to the sale or transfer of behavioural data, including to third-party ad networks and absolute opt-in on company transfer of ownership.
  5. A requirement that advertising should be targeted to content, [user bought fridge A] not through jigsaw data held on users by the company [how user uses fridge A, B, C and related behaviour].
  6. An absolute rejection of using children’s personal data gathered to target advertising and marketing at children

Background: Starting points before privacy

After a brief recap on 5 years ago, we heard two talks.

The first was a presentation from Bosch. They used the insights from the IoT open definition from 5 years ago in their IoT thinking and embedded it in their brand book. The presenter suggested that in five years time, every fridge Bosch sells will be ‘smart’. And the  second was a fascinating presentation, of both EU thinking and the intellectual nudge to think beyond the practical and think what kind of society we want to see using the IoT in future. Hints of hardcore ethics and philosophy that made my brain fizz from , soon to retire from the European Commission.

The principles of open sourcing, manufacturing, and sustainable life cycle were debated in the afternoon with intense arguments and clearly knowledgeable participants, including those who were quiet.  But while the group had assigned security, and started work on it weeks before, there was no one pre-assigned to privacy. For me, that said something. If they are serious about those who earn the trustmark being better for customers than their competition, then there needs to be greater emphasis on thinking like their customers, and by their customers, and what use the mark will be to customers, not companies. Plan early public engagement and testing into the design of this IoT mark, and make that testing open and diverse.

To that end, I believe it needed to be articulated more strongly, that sustainable public trust is the primary goal of the principles.

  • Trust that my device will not become unusable or worthless through updates or lack of them.
  • Trust that my device is manufactured safely and ethically and with thought given to end of life and the environment.
  • Trust that my source components are of high standards.
  • Trust in what data and how that data is gathered and used by the manufacturers.

Fundamental to ‘smart’ devices is their connection to the Internet, and so the last for me, is therefore key to successful public perception and it actually making a difference, beyond the PR value to companies. The value-add must be measured from consumers point of view.

All the openness about design functions and practice improvements, without attempting to change privacy infringing practices, may be wasted effort. Why? Because the perceived benefit of the value of the mark, will be proportionate to what risks it is seen to mitigate.

Why?

Because I assume that you know where your source components come from today. I was shocked to find out not all do and that ‘one degree removed’ is going to be an improvement? Holy cow, I thought. What about regulatory requirements for product safety recalls? These differ of course for different product areas, but I was still surprised. Having worked in global Fast Moving Consumer Goods (FMCG) and food industry, semiconductor and optoelectronics, and medical devices it was self-evident for me, that sourcing is rigorous. So that new requirement to know one degree removed, was a suggested minimum. But it might shock consumers to know there is not usually more by default.

Customers also believe they have reasonable expectations of not being screwed by a product update, left with something that does not work because of its computing based components. The public can take vocal, reputation-damaging action when they are let down.

In the last year alone, some of the more notable press stories include a manufacturer denying service, telling customers, “Your unit will be denied server connection,” after a critical product review. Customer support at Jawbone came in for criticism after reported failings. And even Apple has had problems in rolling out major updates.

While these are visible, the full extent of the overreach of company market and product surveillance into our whole lives, not just our living rooms, is yet to become understood by the general population. What will happen when it is?

The Internet of Things is exacerbating the power imbalance between consumers and companies, between government and citizens. As Wendy Grossman wrote recently, in one sense this may make privacy advocates’ jobs easier. It was always hard to explain why “privacy” mattered. Power, people understand.

That public discussion is long overdue. If open principles on IoT devices mean that the signed-up companies differentiate themselves by becoming market leaders in transparency, it will be a great thing. Companies need to offer full disclosure of data use in any privacy notices in clear, plain language  under GDPR anyway, but to go beyond that, and offer customers fair presentation of both risks and customer benefits, will not only be a point-of-sales benefit, but potentially improve digital literacy in customers too.

The morning discussion touched quite often on pay-for-privacy models. While product makers may see this as offering a good thing, I strove to bring discussion back to first principles.

Privacy is a human right. There can be no ethical model of discrimination based on any non-consensual invasion of privacy. Privacy is not something I should pay to have. You should not design products that reduce my rights. GDPR requires privacy-by-design and data protection by default. Now is that chance for IoT manufacturers to lead that shift towards higher standards.

We also need a new ethics thinking on acceptable fair use. It won’t change overnight, and perfect may be the enemy of better. But it’s not a battle that companies should think consumers have lost. Human rights and information security should not be on the battlefield at all in the war to win customer loyalty.  Now is the time to do better, to be better, demand better for us and in particular, for our children.

Privacy will be a genuine market differentiator

If manufacturers do not want to change their approach to exploiting customer data, they are unlikely to be seen to have changed.

Today feelings that people in US and Europe reflect in surveys are loss of empowerment, feeling helpless, and feeling used. That will shift to shock, resentment, and any change curve will predict, anger.

A 2014 survey for the Royal Statistical Society by Ipsos MORI, found that trust in institutions to use data is much lower than trust in them in general.

“The poll of just over two thousand British adults carried out by Ipsos MORI found that the media, internet services such as social media and search engines and telecommunication companies were the least trusted to use personal data appropriately.” [2014, Data trust deficit with lessons for policymakers, Royal Statistical Society]

In the British student population, one 2015 survey of university applicants in England, found of 37,000 who responded, the vast majority of UCAS applicants agree that sharing personal data can benefit them and support public benefit research into university admissions, but they want to stay firmly in control. 90% of respondents said they wanted to be asked for their consent before their personal data is provided outside of the admissions service.

In 2010, a multi method model of research with young people aged 14-18, by the Royal Society of Engineering, found that, “despite their openness to social networking, the Facebook generation have real concerns about the privacy of their medical records.” [2010, Privacy and Prejudice, RAE, Wellcome]

When people use privacy settings on Facebook set to maximum, they believe they get privacy, and understand little of what that means behind the scenes.

Are there tools designed by others, like Projects by If licenses, and ways this can be done, that you’re not even considering yet?

What if you don’t do it?

“But do you feel like you have privacy today?” I was asked the question in the afternoon. How do people feel today, and does it matter? Companies exploiting consumer data and getting caught doing things the public don’t expect with their data, has repeatedly damaged consumer trust. Data breaches and lack of information security have damaged consumer trust. Both cause reputational harm. Damage to reputation can harm customer loyalty. Damage to customer loyalty costs sales, profit and upsets the Board.

Where overreach into our living rooms has raised awareness of invasive data collection, we are yet to be able to see and understand the invasion of privacy into our thinking and nudge behaviour, into our perception of the world on social media, the effects on decision making that data analytics is enabling as data shows companies ‘how we think’, granting companies access to human minds in the abstract, even before Facebook is there in the flesh.

Governments want to see how we think too, and is thought crime really that far away using database labels of ‘domestic extremists’ for activists and anti-fracking campaigners, or the growing weight of policy makers attention given to predpol, predictive analytics, the [formerly] Cabinet Office Nudge Unit, Google DeepMind et al?

Had the internet remained decentralized the debate may be different.

I am starting to think of the IoT not as the Internet of Things, but as the Internet of Tracking. If some have their way, it will be the Internet of Thinking.

Considering our centralised Internet of Things model, our personal data from human interactions has become the network infrastructure, and data flows, are controlled by others. Our brains are the new data servers.

In the Internet of Tracking, people become the end nodes, not things.

And it is this where the future users will be so important. Do you understand and plan for factors that will drive push back, and crash of consumer confidence in your products, and take it seriously?

Companies have a choice to act as Empires would – multinationals, joining up even on low levels, disempowering individuals and sucking knowledge and power at the centre. Or they can act as Nation states ensuring citizens keep their sovereignty and control over a selected sense of self.

Look at Brexit. Look at the GE2017. Tell me, what do you see is the direction of travel? Companies can fight it, but will not defeat how people feel. No matter how much they hope ‘nudge’ and predictive analytics might give them this power, the people can take back control.

What might this desire to take-back-control mean for future consumer models? The afternoon discussion whilst intense, reached fairly simplistic concluding statements on privacy. We could have done with at least another hour.

Some in the group were frustrated “we seem to be going backwards” in current approaches to privacy and with GDPR.

But if the current legislation is reactive because companies have misbehaved, how will that be rectified for future? The challenge in the IoT both in terms of security and privacy, AND in terms of public perception and reputation management, is that you are dependent on the behaviours of the network, and those around you. Good and bad. And bad practices by one, can endanger others, in all senses.

If you believe that is going back to reclaim a growing sense of citizens’ rights, rather than accepting companies have the outsourced power to control the rights of others, that may be true.

There was a first principle asked whether any element on privacy was needed at all, if the text was simply to state, that the supplier of this product or service must be General Data Protection Regulation (GDPR) compliant. The GDPR was years in the making after all. Does it matter more in the IoT and in what ways? The room tended, understandably, to talk about it from the company perspective.  “We can’t” “won’t” “that would stop us from XYZ.” Privacy would however be better addressed from the personal point of view.

What do people want?

From the company point of view, the language is different and holds clues. Openness, control, and user choice and pay for privacy are not the same thing as the basic human right to be left alone. Afternoon discussion reminded me of the 2014 WAPO article, discussing Mark Zuckerberg’s theory of privacy and a Palo Alto meeting at Facebook:

“Not one person ever uttered the word “privacy” in their responses to us. Instead, they talked about “user control” or “user options” or promoted the “openness of the platform.” It was as if a memo had been circulated that morning instructing them never to use the word “privacy.””

In the afternoon working group on privacy, there was robust discussion whether we had consensus on what privacy even means. Words like autonomy, control, and choice came up a lot. But it was only a beginning. There is opportunity for better. An academic voice raised the concept of sovereignty with which I agreed, but how and where  to fit it into wording, which is at once both minimal and applied, and under a scribe who appeared frustrated and wanted a completely different approach from what he heard across the group, meant it was left out.

This group do care about privacy. But I wasn’t convinced that the room cared in the way that the public as a whole does, but rather only as consumers and customers do. But IoT products will affect potentially everyone, even those who do not buy your stuff. Everyone in that room, agreed on one thing. The status quo is not good enough. What we did not agree on, was why, and what was the minimum change needed to make a enough of a difference that matters.

I share the deep concerns of many child rights academics who see the harm that efforts to avoid restrictions Article 8 the GDPR will impose. It is likely to be damaging for children’s right to access information, be discriminatory according to parents’ prejudices or socio-economic status, and ‘cheating’ – requiring secrecy rather than privacy, in attempts to hide or work round the stringent system.

In ‘The Class’ the research showed, ” teachers and young people have a lot invested in keeping their spheres of interest and identity separate, under their autonomous control, and away from the scrutiny of each other.” [2016, Livingstone and Sefton-Green, p235]

Employers require staff use devices with single sign including web and activity tracking and monitoring software. Employee personal data and employment data are blended. Who owns that data, what rights will employees have to refuse what they see as excessive, and is it manageable given the power imbalance between employer and employee?

What is this doing in the classroom and boardroom for stress, anxiety, performance and system and social avoidance strategies?

A desire for convenience creates shortcuts, and these are often met using systems that require a sign-on through the platforms giants: Google, Facebook, Twitter, et al. But we are kept in the dark how by using these platforms, that gives access to them, and the companies, to see how our online and offline activity is all joined up.

Any illusion of privacy we maintain, we discussed, is not choice or control if based on ignorance, and backlash against companies lack of efforts to ensure disclosure and understanding is growing.

“The lack of accountability isn’t just troubling from a philosophical perspective. It’s dangerous in a political climate where people are pushing back at the very idea of globalization. There’s no industry more globalized than tech, and no industry more vulnerable to a potential backlash.”

[Maciej Ceglowski, Notes from an Emergency, talk at re.publica]

Why do users need you to know about them?

If your connected *thing* requires registration, why does it? How about a commitment to not forcing one of these registration methods or indeed any at all? Social Media Research by Pew Research in 2016 found that  56% of smartphone owners ages 18 to 29 use auto-delete apps, more than four times the share among those 30-49 (13%) and six times the share among those 50 or older (9%).

Does that tell us anything about the demographics of data retention preferences?

In 2012, they suggested social media has changed the public discussion about managing “privacy” online. When asked, people say that privacy is important to them; when observed, people’s actions seem to suggest otherwise.

Does that tell us anything about how well companies communicate to consumers how their data is used and what rights they have?

There is also data with strong indications about how women act to protect their privacy more but when it comes to basic privacy settings, users of all ages are equally likely to choose a private, semi-private or public setting for their profile. There are no significant variations across age groups in the US sample.

Now think about why that matters for the IoT? I wonder who makes the bulk of purchasing decsions about household white goods for example and has Bosch factored that into their smart-fridges-only decision?

Do you *need* to know who the user is? Can the smart user choose to stay anonymous at all?

The day’s morning challenge was to attend more than one interesting discussion happening at the same time. As invariably happens, the session notes and quotes are always out of context and can’t possibly capture everything, no matter how amazing the volunteer (with thanks!). But here are some of the discussion points from the session on the body and health devices, the home, and privacy. It also included a discussion on racial discrimination, algorithmic bias, and the reasons why care.data failed patients and failed as a programme. We had lengthy discussion on ethics and privacy: smart meters, objections to models of price discrimination, and why pay-for-privacy harms the poor by design.

Smart meter data can track the use of unique appliances inside a person’s home and intimate patterns of behaviour. Information about our consumption of power, what and when every day, reveals  personal details about everyday lives, our interactions with others, and personal habits.

Why should company convenience come above the consumer’s? Why should government powers, trump personal rights?

Smart meter is among the knowledge that government is exploiting, without consent, to discover a whole range of issues, including ensuring that “Troubled Families are identified”. Knowing how dodgy some of the school behaviour data might be, that helps define who is “troubled” there is a real question here, is this sound data science? How are errors identified? What about privacy? It’s not your policy, but if it is your product, what are your responsibilities?

If companies do not respect children’s rights,  you’d better shape up to be GDPR compliant

For children and young people, more vulnerable to nudge, and while developing their sense of self can involve forming, and questioning their identity, these influences need oversight or be avoided.

In terms of GDPR, providers are going to pay particular attention to Article 8 ‘information society services’ and parental consent, Article 17 on profiling,  and rights to restriction of processing (19) right to erasure in recital 65 and rights to portability. (20) However, they  may need to simply reassess their exploitation of children and young people’s personal data and behavioural data. Article 57 requires special attention to be paid by regulators to activities specifically targeted at children, as ‘vulnerable natural persons’ of recital 75.

Human Rights, regulations and conventions overlap in similar principles that demand respect for a child, and right to be let alone:

(a) The development of the child ‘s personality, talents and mental and physical abilities to their fullest potential;

(b) The development of respect for human rights and fundamental freedoms, and for the principles enshrined in the Charter of the United Nations.

A weakness of the GDPR is that it allows derogation on age and will create inequality and inconsistency  for children as a result. By comparison Article one of the Convention on the Rights of the Child (CRC) defines who is to be considered a “child” for the purposes of the CRC, and states that: “For the purposes of the present Convention, a child means every human being below the age of eighteen years unless, under the law applicable to the child, majority is attained earlier.”<

Article two of the CRC says that States Parties shall respect and ensure the rights set forth in the present Convention to each child within their jurisdiction without discrimination of any kind.

CRC Article 16 says that no child shall be subjected to arbitrary or unlawful interference with his or her honour and reputation.

Article 8 CRC requires respect for the right of the child to preserve his or her identity […] without unlawful interference.

Article 12 CRC demands States Parties shall assure to the child who is capable of forming his or her own views the right to express those views freely in all matters affecting the child, the views of the child being given due weight in accordance with the age and maturity of the child.

That stands in potential conflict with GDPR article 8. There is much on GDPR on derogations by country, and or children, still to be set.

What next for our data in the wild

Hosting the event at the zoo offered added animals, and during a lunch tour we got out on a tour, kindly hosted by a fellow participant. We learned how smart technology was embedded in some of the animal enclosures, and work on temperature sensors with penguins for example. I love tigers, so it was a bonus that we got to see such beautiful and powerful animals up close, if a little sad for their circumstances and as a general basic principle, seeing big animals caged as opposed to in-the-wild.

Freedom is a common desire in all animals. Physical, mental, and freedom from control by others.

I think any manufacturer that underestimates this element of human instinct is ignoring the ‘hidden dragon’ that some think is a myth.  Privacy is not dead. It is not extinct, or even unlike the beautiful tigers, endangered. Privacy in the IoT at its most basic, is the right to control our purchasing power. The ultimate people power waiting to be sprung. Truly a crouching tiger. People object to being used and if companies continue to do so without full disclosure, they do so at their peril. Companies seem all-powerful in the battle for privacy, but they are not.  Even insurers and data brokers must be fair and lawful, and it is for regulators to ensure that practices meet the law.

When consumers realise our data, our purchasing power has the potential to control, not be controlled, that balance will shift.

“Paper tigers” are superficially powerful but are prone to overextension that leads to sudden collapse. If that happens to the superficially powerful companies that choose unethical and bad practice, as a result of better data privacy and data ethics, then bring it on.

I hope that the IoT mark can champion best practices and make a difference to benefit everyone.

While the companies involved in its design may be interested in consumers, I believe it could be better for everyone, done well. The great thing about the efforts into an #IoTmark is that it is a collective effort to improve the whole ecosystem.

I hope more companies will realise their privacy rights and ethical responsibility in the world to all people, including those interested in just being, those who want to be let alone, and not just those buying.

“If a cat is called a tiger it can easily be dismissed as a paper tiger; the question remains however why one was so scared of the cat in the first place.”

The Resistance to Theory (1982), Paul de Man

Further reading: Networks of Control – A Report on Corporate Surveillance, Digital Tracking, Big Data & Privacy by Wolfie Christl and Sarah Spiekermann

The power behind today’s AI in public services

The power behind today’s AI in public services

Thinking about whether education in England is preparing us for the jobs of the future, means also thinking about how technology will influence it.

Time and again, thinking and discussion about these topics is siloed. At the Turing Institute, the Royal Society, the ADRN and EPSRC, in government departments, discussions on data, or within education practitioner, and public circles — we are all having similar discussions about data and ethics, but with little ownership and no goals for future outcomes. If government doesn’t get it, or have time for it, or policy lacks ethics by design, is it in the public interest for private companies, Google et al., to offer a fait accompli?

There is lots of talking about Machine Learning (ML), Artificial Intelligence (AI) and ethics. But what is being done to ensure that real values — respect for rights, human dignity, and autonomy — are built into practice in the public services delivery?

In most recent data policy it is entirely absent. The Digital Economy Act s33 risks enabling, through removal of inter and intra-departmental data protections, an unprecedented expansion of public data transfers, with “untrammelled powers”. Powers without codes of practice, promised over a year ago. That has fall out for the trustworthiness of legislative process, and data practices across public services.

Predictive analytics is growing but poorly understood in the public and public sector.

There is already dependence on computers in aspects of public sector work. Its interactions with others in sensitive situations demands better knowledge of how systems operate and can be wrong. Debt recovery, and social care to take two known examples.

Risk averse, staff appear to choose not to question the outcome of ‘algorithmic decision making’ or do not have the ability to do so. There is reportedly no analysis training for practitioners, to understand the basis or bias of conclusions. This has the potential that instead of making us more informed, decision-making by machine makes us humans less clever.

What does it do to professionals, if they feel therefore less empowered? When is that a good thing if it overrides discriminatory human decisions? How can we tell the difference and balance these risks if we don’t understand or feel able to challenge them?

In education, what is it doing to children whose attainment is profiled, predicted, and acted on to target extra or less focus from school staff, who have no ML training and without informed consent of pupils or parents?

If authorities use data in ways the public do not expect, such as to ID homes of multiple occupancy without informed consent, they will fail the future to deliver uses for good. The ‘public interest’, ‘user need,’ and ethics can come into conflict according to your point of view. The public and data protection law and ethics object to harms from use of data. This type of application has potential to be mind-blowingly invasive and reveal all sorts of other findings.

Widely informed thinking must be made into meaningful public policy for the greatest public good

Our politicians are caught up in the General Election and buried in Brexit.

Meanwhile, the commercial companies taking AI first rights to capitalise on existing commercial advantage could potentially strip public assets, use up our personal data and public trust, and leave the public with little public good. We are already used by global data players, and by machine-based learning companies, without our knowledge or consent. That knowledge can be used to profit business models, that pay little tax into the public purse.

There are valid macro economic arguments about whether private spend and investment are preferable compared with a state’s ability to do the same. But these companies make more than enough to do it all. Does it signal a failure to a commitment to the wider community; not paying just amounts of taxes, is it a red flag to a company’s commitment to public good?

What that public good should look like, depends on who is invited to participate in the room, and not to tick boxes, but to think and to build.

The Royal Society’s Report on AI and Machine Learning published on April 25, showed a working group of 14 participants, including two Google DeepMind representatives, one from Amazon, private equity investors, and academics from cognitive science and genetics backgrounds.

Our #machinelearning working group chair, professor Peter Donnelly FRS, on today’s major #RSMachinelearning report https://t.co/PBYjzlESmB pic.twitter.com/RM9osnvOMX

— The Royal Society (@royalsociety) April 25, 2017

If we are going to form objective policies the inputs that form the basis for them must be informed, but must also be well balanced, and be seen to be balanced. Not as an add on, but be in the same room.

As Natasha Lomas in TechCrunch noted, “Public opinion is understandably a big preoccupation for the report authors — unsurprisingly so, given that a technology that potentially erodes people’s privacy and impacts their jobs risks being drastically unpopular.”

“The report also calls on researchers to consider the wider impact of their work and to receive training in recognising the ethical implications.”

What are those ethical implications? Who decides which matter most? How do we eliminate recognised discriminatory bias? What should data be used for and AI be working on at all? Who is it going to benefit? What questions are we not asking? Why are young people left out of this debate?

Who decides what the public should or should not know?

AI and ML depend on data. Data is often talked about as a panacea to problems of better working together. But data alone does not make people better informed. In the same way that they fail, if they don’t feel it is their job to pick up the fax. A fundamental building block of our future public and private prosperity is understanding data and how we, and the AI, interact. What is data telling us and how do we interpret it, and know it is accurate?

How and where will we start to educate young people about data and ML, if not about their own and use by government and commercial companies?

The whole of Chapter 5 in the report is very good as a starting point for policy makers who have not yet engaged in the area. Privacy while summed up too short in conclusions, is scattered throughout.

Blind spots remain, however.

  • Over willingness to accommodate existing big private players as their expertise leads design, development and a desire to ‘re-write regulation’.
  • Slowness to react to needed regulation in the public sector (caught up in Brexit) while commercial drivers and technology change forge ahead
  • ‘How do we develop technology that benefits everyone’ must not only think UK, but global South, especially in the bias in how AI is being to taught, and broad socio-economic barriers in application
  • Predictive analytics and professional application = unwillingness to question the computer result. In children’s social care this is already having a damaging upturn in the family courts (S31)
  • Data and technology knowledge and ethics training, must be embedded across the public sector, not only post grad students in machine learning.
  • Harms being done to young people today and potential for intense future exploitation, are being ignored by policy makers and some academics. Safeguarding is often only about blocking in case of liability to the provider, stopping children seeing content, or preventing physical exploitation. It ignores exploitation by online platform firms, and app providers and games creators, of a child’s synthesised online life and use. Laws and government departments’ own practices can be deeply flawed.
  • Young people are left out of discussions which, after all, are about their future. [They might have some of the best ideas, we miss at our peril.]

There is no time to waste

Children and young people have the most to lose while their education, skills, jobs market, economy, culture, care, and society goes through a series of gradual but seismic shift in purpose, culture, and acceptance before finding new norms post-Brexit. They will also gain the most if the foundations are right. One of these must be getting age verification right in GDPR, not allowing it to enable a massive data grab of child-parent privacy.

Although the RS Report considers young people in the context of a future workforce who need skills training, they are otherwise left out of this report.

“The next curriculum reform needs to consider the educational needs of young people through the lens of the implications of machine learning and associated technologies for the future of work.”

Yes it does, but it must give young people and the implications of ML broader consideration for their future, than classroom or workplace.

Facebook has targeted vulnerable young people, it is alleged, to facilitate predatory advertising practices. Some argue that emotive computing or MOOCs belong in the classroom. Who decides?

We are not yet talking about the effects of teaching technology to learn, and its effect on public services and interactions with the public. Questions that Sam Smith asked in Shadow of the smart machine: Will machine learning end?

At the end of this Information Age we are at a point when machine learning, AI and biotechnology are potentially life enhancing or could have catastrophic effects, if indeed “AI will cause people ‘more pain than happiness” as described by Alibaba’s founder Jack Ma.

The conflict between commercial profit and public good, what commercial companies say they will do and actually do, and fears and assurances over predicted outcomes is personified in the debate between Demis Hassabis, co-founder of DeepMind Technologies, (a London-based machine learning AI startup), and Elon Musk, discussing the perils of artificial intelligence.

Vanity Fair reported that, Elon Musk began warning about the possibility of A.I. running amok three years ago. It probably hadn’t eased his mind when one of Hassabis’s partners in DeepMind, Shane Legg, stated flatly, “I think human extinction will probably occur, and technology will likely play a part in this.””

Musk was of the opinion that A.I. was probably humanity’s “biggest existential threat.”

We are not yet joining up multi disciplinary and cross sector discussions of threats and opportunities

Jobs, shift in needed skill sets for education, how we think, interact, value each other, accept or reject ownership and power models; and later, from the technology itself. We are not yet talking conversely, the opportunities that the seismic shifts offer in real terms. Or how and why to accept or reject or regulate them.

Where private companies are taking over personal data given in trust to public services, it is reckless for the future of public interest research to assume there is no public objection. How can we object, if not asked? How can children make an informed choice? How will public interest be assured to be put ahead of private profit? If it is intended on balance to be all about altruism from these global giants, then they must be open and accountable.

Private companies are shaping how and where we find machine learning and AI gathering data about our behaviours in our homes and public spaces.

SPACE10, an innovation hub for IKEA is currently running a survey on how the public perceives and “wants their AI to look, be, and act”, with an eye on building AI into their products, for us to bring flat-pack into our houses.

As the surveillance technology built into the Things in our homes attached to the Internet becomes more integral to daily life, authorities are now using it to gather evidence in investigations; from mobile phones, laptops, social media, smart speakers, and games. The IoT so far seems less about the benefits of collaboration, and all about the behavioural data it collects and uses to target us to sell us more things. Our behaviours tell much more than how we act. They show how we think inside the private space of our minds.

Do you want Google to know how you think and have control over that? The companies of the world that have access to massive amounts of data, and are using that data to now teach AI how to ‘think’. What is AI learning? And how much should the State see or know about how you think, or try to predict it?

Who cares, wins?

It is not overstated to say society and future public good of public services, depends on getting any co-dependencies right. As I wrote in the time of care.data, the economic value of data, personal rights and the public interest are not opposed to one another, but have synergies and co-dependency. One player getting it wrong, can create harm for all. Government must start to care about this, beyond the side effects of saving political embarrassment.

Without joining up all aspects, we cannot limit harms and make the most of benefits. There is nuance and unknowns. There is opaque decision making and secrecy, packaged in the wording of commercial sensitivity and behind it, people who can be brilliant but at the end of the day, are also, human, with all our strengths and weaknesses.

And we can get this right, if data practices get better, with joined up efforts.

Our future society, as our present, is based on webs of trust, on our social networks on- and offline, that enable business, our education, our cultural, and our interactions. Children must trust they will not be used by systems. We must build trustworthy systems that enable future digital integrity.

The immediate harm that comes from blind trust in AI companies is not their AI, but the hidden powers that commercial companies have to nudge public and policy maker behaviours and acceptance, towards private gain. Their ability and opportunity to influence regulation and future direction outweighs most others. But lack of transparency about their profit motives is concerning. Carefully staged public engagement is not real engagement but a fig leaf to show ‘the public say yes’.

The unwillingness by Google DeepMind, when asked at their public engagement event, to discuss their past use of NHS patient data, or the profit model plan or their terms of NHS deals with London hospitals, should be a warning that these questions need answers and accountability urgently.

As TechCrunch suggested after the event, this is all “pretty standard playbook for tech firms seeking to workaround business barriers created by regulation.” Calls for more data, might mean an ever greater power shift.

Companies that have already extracted and benefited from personal data in the public sector, have already made private profit. They and their machines have learned for their future business product development.

A transparent accountable future for all players, private and public, using public data is a necessary requirement for both the public good and private profit. It is not acceptable for departments to hide their practices, just as it is unacceptable if firms refuse algorithmic transparency.

Rebooting antitrust for the information age will not be easy. It will entail new risks: more data sharing, for instance, could threaten privacy. But if governments don’t want a data economy dominated by a few giants, they will need to act soon.” [The Economist, May 6]

If the State creates a single data source of truth, or private Giant tech thinks it can side-step regulation and gets it wrong, their practices screw up public trust. It harms public interest research, and with it our future public good.

But will they care?

If we care, then across public and private sectors, we must cherish shared values and better collaboration. Embed ethical human values into development, design and policy. Ensure transparency of where, how, who and why my personal data has gone.

We must ensure that as the future becomes “smarter”, we educate ourselves and our children to stay intelligent about how we use data and AI.

We must start today, knowing how we are used by both machines, and man.


First published on Medium for a change.

The perfect storm: three bills that will destroy student data privacy in England

Lords have voiced criticism and concern at plans for ‘free market’ universities, that will prioritise competition over collaboration and private interests over social good. But while both Houses have identified the institutional effects, they are yet to discuss the effects on the individuals of a bill in which “too much power is concentrated in the centre”.

The Higher Education and Research Bill sucks in personal data to the centre, as well as power. It creates an authoritarian panopticon of the people within the higher education and further education systems. Section 1, parts 72-74 creates risks but offers no safeguards.

Applicants and students’ personal data is being shifted into a  top-down management model, at the same time as the horizontal safeguards for its distribution are to be scrapped.

Through deregulation and the building of a centralised framework, these bills will weaken the purposes for which personal data are collected, and weaken existing requirements on consent to which the data may be used at national level. Without amendments, every student who enters this system will find their personal data used at the discretion of any future Secretary of State for Education without safeguards or oversight, and forever. Goodbye privacy.

Part of the data extraction plans are for use in public interest research in safe settings with published purpose, governance, and benefit. These are well intentioned and this year’s intake of students will have had to accept that use as part of the service in the privacy policy.

But in addition and separately, the Bill will permit data to be used at the discretion of the Secretary of State, which waters down and removes nuances of consent for what data may or may not be used today when applicants sign up to UCAS.

Applicants today are told in the privacy policy they can consent separately to sharing their data with the Student Loans company for example. This Bill will remove that right when it permits all Applicant data to be used by the State.

This removal of today’s consent process denies all students their rights to decide who may use their personal data beyond the purposes for which they permit its sharing.

And it explicitly overrides the express wishes registered by the 28,000 applicants, 66% of respondents to a 2015 UCAS survey, who said as an example, that they should be asked before any data was provided to third parties for student loan applications (or even that their data should never be provided for this).

Not only can the future purposes be changed without limitation,  by definition, when combined with other legislation, namely the Digital Economy Bill that is in the Lords at the same time right now, this shift will pass personal data together with DWP and in connection with HMRC data expressly to the Student Loans Company.

In just this one example, the Higher Education and Research Bill is being used as a man in the middle. But it will enable all data for broad purposes, and if those expand in future, we’ll never know.

This change, far from making more data available to public interest research, shifts the balance of power between state and citizen and undermines the very fabric of its source of knowledge; the creation and collection of personal data.

Further, a number of amendments have been proposed in the Lords to clause 9 (the transparency duty) which raise more detailed privacy issues for all prospective students, concerns UCAS share.

Why this lack of privacy by design is damaging

This shift takes away our control, and gives it to the State at the very time when ‘take back control’ is in vogue. These bills are building a foundation for a data Brexit.

If the public does not trust who will use it and why or are told that when they provide data they must waive any rights to its future control, they will withhold or fake data. 8% of applicants even said it would put them off applying through UCAS at all.

And without future limitation, what might be imposed is unknown.

This shortsightedness will ultimately cause damage to data integrity and the damage won’t come in education data from the Higher Education Bill alone. The Higher Education and Research Bill is just one of three bills sweeping through Parliament right now which build a cumulative anti-privacy storm together, in what is labelled overtly as data sharing legislation or is hidden in tucked away clauses.

The Technical and Further Education Bill – Part 3

In addition to entirely new Applicant datasets moving from UCAS to the DfE in clauses 73 and 74 of the  Higher Education and Research Bill,  Apprentice and FE student data already under the Secretary of State for Education will see potentially broader use under changed purposes of Part 3 of the Technical and Further Education Bill.

Unlike the Higher Education and Research Bill, it may not fundamentally changing how the State gathers information on further education, but it has the potential to do so on use.

The change is a generalisation of purposes. Currently, subsection 1 of section 54 refers to “purposes of the exercise of any of the functions of the Secretary of State under Part 4 of the Apprenticeships, Skills, Children and Learning Act 2009”.

Therefore, the government argues, “it would not hold good in circumstances where certain further education functions were transferred from the Secretary of State to some combined authorities in England, which is due to happen in 2018.”<

This is why clause 38 will amend that wording to “purposes connected with further education”.

Whatever the details of the reason, the purposes are broader.

Again, combined with the Digital Economy Bill’s open ended purposes, it means the Secretary of State could agree to pass these data on to every other government department, a range of public bodies, and some private organisations.

The TFE BIll is at Report stage in the House of Commons on January 9, 2017.

What could go possibly go wrong?

These loose purposes, without future restrictions, definitions of third parties it could be given to or why, or clear need to consult the public or parliament on future scope changes, is a  repeat of similar legislative changes which have resulted in poor data practices using school pupil data in England age 2-19 since 2000.

Policy makers should consider whether the intent of these three bills is to give out identifiable, individual level, confidential data of young people under 18, for commercial use without their consent? Or to journalists and charities access? Should it mean unfettered access by government departments and agencies such as police and Home Office Removals Casework teams without any transparent register of access, any oversight, or accountability?

These are today’s uses by third-parties of school children’s individual, identifiable and sensitive data from the National Pupil Database.

Uses of data not as statistics, but named individuals for interventions in individual lives.

If the Home Secretaries past and present have put international students at the centre of plans to cut migration to the tens of thousands and government refuses to take student numbers out of migration figures, despite them being seen as irrelevant in the substance of the numbers debate, this should be deeply worrying.

Will the MOU between the DfE and the Home Office Removals Casework team be a model for access to all student data held at the Department for Education, even all areas of public administrative data?

Hoping that the data transfers to the Home Office won’t result in the deportation of thousands we would not predict today, may be naive.

Under the new open wording, the Secretary of State for Education might even  decide to sell the nation’s entire Technical and Further Education student data to Trump University for the purposes of their ‘research’ to target marketing at UK students or institutions that may be potential US post-grad applicants. The Secretary of State will have the data simply because she “may require [it] for purposes connected with further education.”

And to think US buyers or others would not be interested is too late.

In 2015 Stanford University made a request of the National Pupil Database for both academic staff and students’ data. It was rejected. We know this only from the third party release register. Without any duty to publish requests, approved users or purposes of data release, where is the oversight for use of these other datasets?

If these are not the intended purposes of these three bills, if there should be any limitation on purposes of use and future scope change, then safeguards and oversight need built into the face of the bills to ensure data privacy is protected and avoid repeating the same again.

Hoping that the decision is always going to be, ‘they wouldn’t approve a request like that’ is not enough to protect millions of students privacy.

The three bills are a perfect privacy storm

As other Europeans seek to strengthen the fundamental rights of their citizens to take back control of their personal data under the GDPR coming into force in May 2018, the UK government is pre-emptively undermining ours in these three bills.

Young people, and data dependent institutions, are asking for solutions to show what personal data is held where, and used by whom, for what purposes. That buys in the benefit message that builds trust showing what you said you’d do with my data, is what you did with my data. [1] [2]

Reality is that in post-truth politics it seems anything goes, on both sides of the Pond. So how will we trust what our data is used for?

2015-16 advice from the cross party Science and Technology Committee suggested data privacy is unsatisfactory, “to be left unaddressed by Government and without a clear public-policy position set out“.  We hear the need for data privacy debated about use of consumer data, social media, and on using age verification. It’s necessary to secure the public trust needed for long term public benefit and for economic value derived from data to be achieved.

But the British government seems intent on shortsighted legislation which does entirely the opposite for its own use: in the Higher Education Bill, the Technical and Further Education Bill and in the Digital Economy Bill.

These bills share what Baroness Chakrabarti said of the Higher Education Bill in its Lords second reading on the 6th December, “quite an achievement for a policy to combine both unnecessary authoritarianism with dangerous degrees of deregulation.”

Unchecked these Bills create the conditions needed for catastrophic failure of public trust. They shift ever more personal data away from personal control, into the centralised control of the Secretary of State for unclear purposes and use by undefined third parties. They jeopardise the collection and integrity of public administrative data.

To future-proof the immediate integrity of student personal data collection and use, the DfE reputation, and public and professional trust in DfE political leadership, action must be taken on safeguards and oversight, and should consider:

  • Transparency register: a public record of access, purposes, and benefits to be achieved from use
  • Subject Access Requests: Providing the public ways to access copies of their own data
  • Consent procedures should be strengthened for collection and cannot say one thing, and do another
  • Ability to withdraw consent from secondary purposes should be built in by design, looking to GDPR from 2018
  • Clarification of the legislative purpose of intended current use by the Secretary of State and its boundaries shoud be clear
  • Future purpose and scope change limitations should require consultation – data collected today must not used quite differently tomorrow without scrutiny and ability to opt out (i.e. population wide registries of religion, ethnicity, disability)
  • Review or sunset clause

If the legislation in these three bills pass without amendment, the potential damage to privacy will be lasting.


[1] http://www.parliament.uk/business/publications/written-questions-answers-statements/written-question/Commons/2016-07-15/42942/  Parliamentary written question 42942 on the collection of pupil nationality data in the school census starting in September 2016:   “what limitations will be placed by her Department on disclosure of such information to (a) other government departments?”

Schools Minister Nick Gibb responded on July 25th 2016: ”

“These new data items will provide valuable statistical information on the characteristics of these groups of children […] “The data will be collected solely for internal Departmental use for the analytical, statistical and research purposes described above. There are currently no plans to share the data with other government Departments”

[2] December 15, publication of MOU between the Home Office  Casework Removals Team and the DfE, reveals “the previous agreement “did state that DfE would provide nationality information to the Home Office”, but that this was changed “following discussions” between the two departments.” http://schoolsweek.co.uk/dfe-had-agreement-to-share-pupil-nationality-data-with-home-office/ 

The agreement was changed on 7th October 2016 to not pass nationality data over. It makes no mention of not using the data within the DfE for the same purposes.

Datasharing, lawmaking and ethics: power, practice and public policy

“Lawmaking is the Wire, not Schoolhouse Rock. It’s about blood and war and power, not evidence and argument and policy.”

"We can't trust the regulators," they say. "We need to be able to investigate the data for ourselves." Technology seems to provide the perfect solution. Just put it all online - people can go through the data while trusting no one.  There's just one problem. If you can't trust the regulators, what makes you think you can trust the data?" 

Extracts from The Boy Who Could Change the World: The Writings of Aaron Swartz. Chapter: ‘When is Technology Useful? ‘ June 2009.

The question keeps getting asked, is the concept of ethics obsolete in Big Data?

I’ve come to some conclusions why ‘Big Data’ use keeps pushing the boundaries of what many people find acceptable, and yet the people doing the research, the regulators and lawmakers often express surprise at negative reactions. Some even express disdain for public opinion, dismissing it as ignorant, not ‘understanding the benefits’, yet to be convinced. I’ve decided why I think what is considered ‘ethical’ in data science does not meet public expectation.

It’s not about people.

Researchers using large datasets, often have a foundation in data science, applied computing, maths, and don’t see data as people. It’s only data. Creating patterns, correlations, and analysis of individual level data are not seen as research involving human subjects.

This is embodied in the nth number of research ethics reviews I have read in the last year in which the question is asked, does the research involve people? The answer given is invariably ‘no’.

And these data analysts using, let’s say health data, are not working in a subject that is founded on any ethical principle, contrasting with the medical world the data come from.

The public feels differently about the information that is about them, and may be known, only to them or select professionals. The values that we as the public attach to our data  and expectations of its handling may reflect the expectation we have of handling of us as people who are connected to it. We see our data as all about us.

The values that are therefore put on data, and on how it can and should be used, can be at odds with one another, the public perception is not reciprocated by the researchers. This may be especially true if researchers are using data which has been de-identified, although it may not be anonymous.

New legislation on the horizon, the Better Use of Data in Government,  intends to fill the [loop]hole between what was legal to share in the past and what some want to exploit today, and emphasises a gap in the uses of data by public interest, academic researchers, and uses by government actors. The first incorporate by-and-large privacy and anonymisation techniques by design, versus the second designed for applied use of identifiable data.

Government departments and public bodies want to identify and track people who are somehow misaligned with the values of the system; either through fraud, debt, Troubled Families, or owing Student Loans. All highly sensitive subjects. But their ethical data science framework will not treat them as individuals, but only as data subjects. Or as groups who share certain characteristics.

The system again intrinsically fails to see these uses of data as being about individuals, but sees them as categories of people – “fraud” “debt” “Troubled families.” It is designed to profile people.

Services that weren’t built for people, but for government processes, result in datasets used in research, that aren’t well designed for research. So we now see attempts to shoehorn historical practices into data use  by modern data science practitioners, with policy that is shortsighted.

We can’t afford for these things to be so off axis, if civil service thinking is exploring “potential game-changers such as virtual reality for citizens in the autism spectrum, biometrics to reduce fraud, and data science and machine-learning to automate decisions.”

In an organisation such as DWP this must be really well designed since “the scale at which we operate is unprecedented: with 800 locations and 85,000  colleagues, we’re larger than most retail operations.”

The power to affect individual lives through poor technology is vast and some impacts seem to be being badly ignored. The ‘‘real time earnings’ database improved accuracy of benefit payments was widely agreed to have been harmful to some individuals through the Universal Credit scheme, with delayed payments meaning families at foodbanks, and contributing to worse.

“We believe execution is the major job of every business leader,” perhaps not the best wording in on DWP data uses.

What accountability will be built-by design?

I’ve been thinking recently about drawing a social ecological model of personal data empowerment or control. Thinking about visualisation of wants, gaps and consent models, to show rather than tell policy makers where these gaps exist in public perception and expectations, policy and practice. If anyone knows of one on data, please shout. I think it might be helpful.

But the data *is* all about people

Regardless whether they are in front of you or numbers on a screen, big or small datasets using data about real lives are data about people. And that triggers a need to treat the data with an ethical approach as you would people involved face-to-face.

Researchers need to stop treating data about people as meaningless data because that’s not how people think about their own data being used. Not only that, but if the whole point of your big data research is to have impact, your data outcomes, will change lives.

Tosh, I know some say. But, I have argued, the reason being is that the applications of the data science/ research/ policy findings / impact of immigration in education review / [insert purposes of the data user’s choosing] are designed to have impact on people. Often the people about whom the research is done without their knowledge or consent. And while most people say that is OK, where it’s public interest research, the possibilities are outstripping what the public has expressed as acceptable, and few seem to care.

Evidence from public engagement and ethics all say, hidden pigeon-holing, profiling, is unacceptable. Data Protection law has special requirements for it, on autonomous decisions. ‘Profiling’ is now clearly defined under article 4 of the GDPR as ” any form of automated processing of personal data consisting of using those data to evaluate certain personal aspects relating to a natural person, in particular to analyse or predict aspects concerning that natural person’s performance at work, economic situation, health, personal preferences, interests, reliability, behaviour, location or movements.”

Using big datasets for research that ‘isn’t interested in individuals’ may still intend to create results profiling groups for applied policing, or discriminate, to make knowledge available by location. The data may have been deidentified, but in application becomes no longer anonymous.

Big Data research that results in profiling groups with the intent for applied health policy impacts for good, may by the very point of research, with the intent of improving a particular ethnic minority access to services, for example.

Then look at the voting process changes in North Carolina and see how that same data, the same research knowledge might be applied to exclude, to restrict rights, and to disempower.

Is it possible to have ethical oversight that can protect good data use and protect people’s rights if they conflict with the policy purposes?

The “clear legal basis”is not enough for public trust

Data use can be legal and can still be unethical, harmful and shortsighted in many ways, for both the impacts on research – in terms of withholding data and falsifying data and avoiding the system to avoid giving in data – and the lives it will touch.

What education has to learn from health is whether it will permit the uses by ‘others’ outside education to jeopardise the collection of school data intended in the best interests of children, not the system. In England it must start to analyse what is needed vs wanted. What is necessary and proportionate and justifies maintaining named data indefinitely, exposed to changing scope.

In health, the most recent Caldicott review suggests scope change by design – that is a red line for many: “For that reason the Review recommends that, in due course, the opt-out should not apply to all flows of information into the HSCIC. This requires careful consideration with the primary care community.”

The community spoke out already, and strongly in Spring and Summer 2014 that there must be an absolute right to confidentiality to protect patients’ trust in the system. Scope that ‘sounds’ like it might sneakily change in future, will be a death knell to public interest research, because repeated trust erosion will be fatal.

Laws change to allow scope change without informing people whose data are being used for different purposes

Regulators must be seen to be trusted, if the data they regulate is to be trustworthy. Laws and regulators that plan scope for the future watering down of public protection, water down public trust from today. Unethical policy and practice, will not be saved by pseudo-data-science ethics.

Will those decisions in private political rooms be worth the public cost to research, to policy, and to the lives it will ultimately affect?

What happens when the ethical black holes in policy, lawmaking and practice collide?

At the last UK HealthCamp towards the end of the day, when we discussed the hard things, the topic inevitably moved swiftly to consent, to building big databases, public perception, and why anyone would think there is potential for abuse, when clearly the intended use is good.

The answer came back from one of the participants, “OK now it’s the time to say. Because, Nazis.” Meaning, let’s learn from history.

Given the state of UK politics, Go Home van policies, restaurant raids, the possibility of Trump getting access to UK sensitive data of all sorts from across the Atlantic, given recent policy effects on the rights of the disabled and others, I wonder if we would hear the gentle laughter in the room in answer to the same question today.

With what is reported as Whitehall’s digital leadership sharp change today, the future of digital in government services and policy and lawmaking does indeed seem to be more “about blood and war and power,” than “evidence and argument and policy“.

The concept of ethics in datasharing using public data in the UK is far from becoming obsolete. It has yet to begin.

We have ethical black holes in big data research, in big data policy, and big data practices in England. The conflicts between public interest research and government uses of population wide datasets, how the public perceive the use of our data and how they are used, gaps and tensions in policy and practice are there.

We are simply waiting for the Big Bang. Whether it will be creative, or destructive we are yet to feel.

*****

image credit: LIGO – graphical visualisation of black holes on the discovery of gravitational waves

References:

Report: Caldicott review – National Data Guardian for Health and Care Review of Data Security, Consent and Opt-Outs 2016

Report: The OneWay Mirror: Public attitudes to commercial access to health data

Royal Statistical Society Survey carried out by Ipsos MORI: The Data Trust Deficit

Can new datasharing laws win social legitimacy, public trust and support without public engagement?

I’ve been struck by stories I’ve heard on the datasharing consultation, on data science, and on data infrastructures as part of ‘government as a platform’ (#GaaPFuture) in recent weeks. The audio recorded by the Royal Statistical Society on March 17th is excellent, and there were some good questions asked.

There were even questions from insurance backed panels to open up more data for commercial users, and calls for journalists to be seen as accredited researchers, as well as to include health data sharing. Three things that some stakeholders, all users of data, feel are  missing from consultation, and possibly some of those with the most widespread public concern and lowest levels of public trust. [1]

What I feel is missing in consultation discussions are:

  1. a representative range of independent public voice
  2. a compelling story of needs – why tailored public services benefits citizens from whom data is taken, not only benefits data users
  3. the impacts we expect to see in local government
  4. any cost/risk/benefit assessment of those impacts, or for citizens
  5. how the changes will be independently evaluated – as some are to be reviewed

The Royal Statistical Society and ODI have good summaries here of their thoughts, more geared towards the statistical and research aspects of data,  infrastructure and the consultation.

I focus on the other strands that use identifiable data for targeted interventions. Tailored public services, Debt, Fraud, Energy Companies’ use. I think we talk too little of people, and real needs.

Why the State wants more datasharing is not yet a compelling story and public need and benefit seem weak.

So far the creation of new data intermediaries, giving copies of our personal data to other public bodies  – and let’s be clear that this often means through commercial representatives like G4S, Atos, Management consultancies and more –  is yet to convince me of true public needs for the people, versus wants from parts of the State.

What the consultation hopes to achieve, is new powers of law, to give increased data sharing increased legal authority. However this alone will not bring about the social legitimacy of datasharing that the consultation appears to seek through ‘open policy making’.

Legitimacy is badly needed if there is to be public and professional support for change and increased use of our personal data as held by the State, which is missing today,  as care.data starkly exposed. [2]

The gap between Social Legitimacy and the Law

Almost 8 months ago now, before I knew about the datasharing consultation work-in-progress, I suggested to BIS that there was an opportunity for the UK to drive excellence in public involvement in the use of public data by getting real engagement, through pro-active consent.

The carrot for this, is achieving the goal that government wants – greater legal clarity, the use of a significant number of consented people’s personal data for complex range of secondary uses as a secondary benefit.

It was ignored.

If some feel entitled to the right to infringe on citizens’ privacy through a new legal gateway because they believe the public benefit outweighs private rights, then they must also take on the increased balance of risk of doing so, and a responsibility to  do so safely. It is in principle a slippery slope. Any new safeguards and ethics for how this will be done are however unclear in those data strands which are for targeted individual interventions. Especially if predictive.

Upcoming discussions on codes of practice [which have still to be shared] should demonstrate how this is to happen in practice, but codes are not sufficient. Laws which enable will be pushed to their borderline of legal and beyond that of ethical.

In England who would have thought that the 2013 changes that permitted individual children’s data to be given to third parties [3] for educational purposes, would mean giving highly sensitive, identifiable data to journalists without pupils or parental consent? The wording allows it. It is legal. However it fails the DPA Act legal requirement of fair processing.  Above all, it lacks social legitimacy and common sense.

In Scotland, there is current anger over the intrusive ‘named person’ laws which lack both professional and public support and intrude on privacy. Concerns raised should be lessons to learn from in England.

Common sense says laws must take into account social legitimacy.

We have been told at the open policy meetings that this change will not remove the need for informed consent. To be informed, means creating the opportunity for proper communications, and also knowing how you can use the service without coercion, i.e. not having to consent to secondary data uses in order to get the service, and knowing to withdraw consent at any later date. How will that be offered with ways of achieving the removal of data after sharing?

The stick for change, is the legal duty that the recent 2015 CJEU ruling reiterating the legal duty to fair processing [4] waved about. Not just a nice to have, but State bodies’ responsibility to inform citizens when their personal data are used for purposes other than those for which those data had initially been consented and given. New legislation will not  remove this legal duty.

How will it be achieved without public engagement?

Engagement is not PR

Failure to act on what you hear from listening to the public is costly.

Engagement is not done *to* people, don’t think explain why we need the data and its public benefit’ will work. Policy makers must engage with fears and not seek to dismiss or diminish them, but acknowledge and mitigate them by designing technically acceptable solutions. Solutions that enable data sharing in a strong framework of privacy and ethics, not that sees these concepts as barriers. Solutions that have social legitimacy because people support them.

Mr Hunt’s promised February 2014 opt out of anonymised data being used in health research, has yet to be put in place and has had immeasurable costs for delayed public research, and public trust.

How long before people consider suing the DH as data controller for misuse? From where does the arrogance stem that decides to ignore legal rights, moral rights and public opinion of more people than those who voted for the Minister responsible for its delay?

 

This attitude is what fails care.data and the harm is ongoing to public trust and to confidence for researchers’ continued access to data.

The same failure was pointed out by the public members of the tiny Genomics England public engagement meeting two years ago in March 2014, called to respond to concerns over the lack of engagement and potential harm for existing research. The comms lead made a suggestion that the new model of the commercialisation of the human genome in England, to be embedded in the NHS by 2017 as standard clinical practice, was like steam trains in Victorian England opening up the country to new commercial markets. The analogy was felt by the lay attendees to be, and I quote, ‘ridiculous.’

Exploiting confidential personal data for public good must have support and good two-way engagement if it is to get that support, and what is said and agreed must be acted on to be trustworthy.

Policy makers must take into account broad public opinion, and that is unlikely to be submitted to a Parliamentary consultation. (Personally, I first knew such  processes existed only when care.data was brought before the Select Committee in 2014.) We already know what many in the public think about sharing their confidential data from the work with care.data and objections to third party access, to lack of consent. Just because some policy makers don’t like what was said, doesn’t make that public opinion any less valid.

We must bring to the table the public voice from past but recent public engagement work on administrative datasharing [5], the voice of the non-research community, and from those who are not stakeholders who will use the data but the ‘data subjects’, the public  whose data are to be used.

Policy Making must be built on Public Trust

Open policy making is not open just because it says it is. Who has been invited, participated, and how their views actually make a difference on content and implementation is what matters.

Adding controversial ideas at the last minute is terrible engagement, its makes the process less trustworthy and diminishes its legitimacy.

This last minute change suggests some datasharing will be dictated despite critical views in the policy making and without any public engagement. If so, we should ask policy makers on what mandate?

Democracy depends on social legitimacy. Once you lose public trust, it is not easy to restore.

Can new datasharing laws win social legitimacy, public trust and support without public engagement?

In my next post I’ll post look at some of the public engagement work done on datasharing to date, and think about ethics in how data are applied.

*************

References:

[1] The Royal Statistical Society data trust deficit

[2] “The social licence for research: why care.data ran into trouble,” by Carter et al.

[3] FAQs: Campaign for safe and ethical National Pupil Data

[4] CJEU Bara 2015 Ruling – fair processing between public bodies

[5] Public Dialogues using Administrative data (ESRC / ADRN)

img credit: flickr.com/photos/internetarchivebookimages/

On the Boundaries of Being Human and Big Data

Atlas, the Boston Dynamics created robot, won hearts and minds this week as it stoically survived man being mean.  Our collective human response was an emotional defence of the machine, and criticism of its unfair treatment by its tester.

Some on Twitter recalled the incident of Lord of The Flies style bullying by children in Japan that led the programmers to create an algorithm for ‘abuse avoidance’.

The concepts of fairness and of decision making algorithms for ‘abuse avoidance’ are interesting from perspectives of data mining, AI and the wider access to and use of tech in general, and in health specifically.

If the decision to avoid abuse can be taken out of an individual’s human hands and are based on unfathomable amounts of big data, where are its limits applied to human behaviour and activity?

When it is decided that an individual’s decision making capability is impaired or has been forfeited their consent may be revoked in their best interest.

Who has oversight of the boundaries of what is acceptable for one person, or for an organisation, to decide what is in someone else’s best interest, or indeed, the public interest?

Where these boundaries overlap – personal abuse avoidance, individual best interest and the public interest – and how society manage them, with what oversight, is yet to be widely debated.

The public will shortly be given the opportunity to respond to plans for the expansion of administrative datasharing in England through consultation.

We must get involved and it must be the start of a debate and dialogue not simply a tick-box to a done-deal, if data derived from us are to be used as a platform for future to “achieve great results for the NHS and everyone who depends on it.”

Administering applied “abuse avoidance” and Restraining Abilities

Administrative uses and secondary research using the public’s personal data are applied not only in health, but across the board of public bodies, including big plans for tech in the justice system.

An example in the news this week of applied tech and its restraint on human behaviour was ankle monitors.  While one type was abandoned by the MOJ at a cost of £23m on the same day more funding for transdermal tags was announced in London.

The use of this technology as a monitoring tool, should not of itself be a punishment. It is said compliance is not intended to affect the dignity of individuals who are being monitored, but through the collection of personal and health data  will ensure the deprivation of alcohol – avoiding its abuse for a person’s own good and in the public interest. Is it fair?

Abstinence orders might be applied to those convicted of crimes such as assault, being drunk and disorderly and drunk driving.

We’re yet to see much discussion of how these varying degrees of integration of tech with the human body, and human enhancement will happen through robot elements in our human lives.

How will the boundaries of what is possible and desirable be determined and by whom with what oversight?

What else might be considered as harmful as alcohol to individuals and to  society? Drugs? Nictotine? Excess sugar?

As we wonder about the ethics of how humanoids will act and the aesthetics of how human they look, I wonder how humane are we being, in all our ‘public’ tech design and deployment?

Umberto Eco who died on Friday wrote in ‘The birth of ethics’ that there are universal ideas on constraints, effectively that people should not harm other people, through deprivation, restrictions or psychological torture. And that we should not impose anything on others that “diminishes or stifles our capacity to think.”

How will we as a society collectively agree what that should look like, how far some can impose on others, without consent?

Enhancing the Boundaries of Being Human

Technology might be used to impose bodily boundaries on some people, but tech can also be used for the enhancement of others. retweeted this week, the brilliant Angel Giuffria’s arm.

While the technology in this case is literally hands-on in its application, increasingly it is not the technology itself but the data that it creates or captures which enables action through data-based decision making.

Robots that are tiny may be given big responsibilities to monitor and report massive amounts of data. What if we could swallow them?

Data if analysed and understood, become knowledge.

Knowledge can be used to inform decisions and take action.

So where are the boundaries of what data may be extracted,  information collated, and applied as individual interventions?

Defining the Boundaries of “in the Public Interest”

Where are boundaries of what data may be created, stored, and linked to create a detailed picture about us as individuals, if the purpose is determined to be in the public interest?

Who decides which purposes are in the public interest? What qualifies as research purposes? Who qualifies as meeting the criteria of ‘researcher’?

How far can research and interventions go without consent?

Should security services and law enforcement agencies always be entitled to get access to individuals’ data ‘in the public interest’?

That’s something Apple is currently testing in the US.

Should research bodies always be entitled to get access to individuals’ data ‘in the public interest’?

That’s something care.data tried and failed to assume the public supported and has yet to re-test. Impossible before respecting the opt out that was promised over two years ago in March 2014.

The question how much data research bodies may be ‘entitled to’ will be tested again in the datasharing consultation in the UK.

How data already gathered are used in research may be used differently from it is when we consent to its use at colllection. How this changes over time and its potential for scope creep is seen in Education. Pupil data has gone from passive collection of name to giving it out to third parties, to use in national surveys, so far.

And what of the future?

Where is the boundary between access and use of data not in enforcement of acts already committed but in their prediction and prevention?

If you believe there should be an assumption of law enforcement access to data when data are used for prediction and prevention, what about health?

Should there be any difference between researchers’ access to data when data are used for past analysis and for use in prediction?

If ethics define the boundary between what is acceptable and where actions by one person may impose something on another that “diminishes or stifles our capacity to think” – that takes away our decision making capacity – that nudges behaviour, or acts on behaviour that has not yet happened, who decides what is ethical?

How does a public that is poorly informed about current data practices, become well enough informed to participate in the debate of how data management should be designed today for their future?

How Deeply Mined should our Personal Data be?

The application of technology, non-specific but not yet AI, was also announced this week in the Google DeepMind work in the NHS.

Its first key launch app co-founder provided a report that established the operating framework for the Behavioural Insights Team established by Prime Minister David Cameron.

A number of highly respected public figures have been engaged to act in the public interest as unpaid Independent Reviewers of Google DeepMind Health. It will be interesting to see what their role is and how transparent its workings and public engagement will be.

The recent consultation on the NHS gave overwhelming feedback that the public does not support the direction of current NHS change. Even having removed all responses associated with ‘lefty’ campaigns, concerns listed on page 11, are consistent including a request the Government “should end further involvement of the private sector in healthcare”. It appears from the response that this engagement exercise will feed little into practice.

The strength of feeling should however be a clear message to new projects that people are passionate that equal access to healthcare for all matters and that the public wants to be informed and have their voices heard.

How will public involvement be ensured as complexity increases in these healthcare add-ons and changing technology?

Will Google DeepMind pave the way to a new approach to health research? A combination of ‘nudge’ behavioural insights, advanced neural networks, Big Data and technology is powerful. How will that power be used?

I was recently told that if new research is not pushing the boundaries of what is possible and permissible then it may not be worth doing, as it’s probably been done before.

Should anything that is new that becomes possible be realised?

I wonder how the balance will be weighted in requests for patient data and their application, in such a high profile project.

Will NHS Research Ethics Committees turn down research proposals in-house in hospitals that benefit the institution or advance their reputation, or the HSCIC, ever feel able to say no to data use by Google DeepMind?

Ethics committees safeguard the rights, safety, dignity and well-being of research participants, independently of research sponsors whereas these representatives are not all independent of commercial supporters. And it has not claimed it’s trying to be an ethics panel. But oversight is certainly needed.

The boundaries of ownership between what is seen to benefit commercial and state in modern health investment is perhaps more than blurred to an untrained eye. Genomics England – the government’s flagship programme giving commercial access to the genome of 100K people –  stockholding companies, data analytics companies, genome analytic companies, genome collection, and human tissue research, commercial and academic research,  often share directors, working partnerships and funders. That’s perhaps unsurprising given such a specialist small world.

It’s exciting to think of the possibilities if, “through a focus on patient outcomes, effective oversight, and the highest ethical principles, we can achieve great results for the NHS and everyone who depends on it.”

Where will an ageing society go, if medics can successfully treat more cancer for example? What diseases will be prioritised and others left behind in what is economically most viable to prevent? How much investment will be made in diseases of the poor or in countries where governments cannot afford to fund programmes?

What will we die from instead? What happens when some causes of ‘preventative death’ are deemed more socially acceptable than others? Where might prevention become socially enforced through nudging behaviour into new socially acceptable or ethical norms?

Don’t be Evil

Given the leading edge of the company and its curiosity-by-design to see how far “can we” will reach, “don’t be evil” may be very important. But “be good” might be better. Where is that boundary?

The boundaries of what ‘being human’ means and how Big Data will decide and influence that, are unclear and changing. How will the law and regulation keep up and society be engaged in support?

Data principles such as fairness, keeping data accurate, complete and up-to-date and ensuring data are not excessive retained for no longer than necessary for the purpose are being widely ignored or exempted under the banner of ‘research’.

Can data use retain a principled approach despite this and if we accept commercial users, profit making based on public data, will those principles from academic research remain in practice?

Exempt from the obligation to give a copy of personal data to an individual on request if data are for ‘research’ purposes, data about us and our children, are extracted and stored ‘without us’. Forever. That means in a future that we cannot see, but Google DeepMind among others, is designing.

Lay understanding, and that of many climical professionals is likely to be left far behind if advanced technologies and use of big data decision-making algorithms are hidden in black boxes.

Public transparency of the use of our data and future planned purposes are needed to create trust that these purposes are wise.

Data are increasingly linked and more valuable when identifiable.

Any organisation that wants to future-proof its reputational risk will make sure data collection and use today is with consent, since future outcomes derived are likely to be in interventions for individuals or society. Catching up consent will be hard unless designed in now.

A Dialogue on the Boundaries of Being Human and Big Data

Where the commercial, personal, and public interests are blurred, the highest ethical principles are going to be needed to ensure ‘abuse avoidance’ in the use of new technology, in increased data linkage and resultant data use in research of many different kinds.

How we as a society achieve the benefits of tech and datasharing and where its boundaries lie in “the public interest” needs public debate to co-design the direction we collectively want to partake in.

Once that is over, change needs supported by a method of oversight that is responsive to new technology, data use, and its challenges.

What a channel for ongoing public dialogue, challenge and potentially recourse might look like, should be part of that debate.

Destination smart-cities: design, desire and democracy (Part two)

Smart cities: private reach in public space and personal lives

Smart-cities are growing in the UK through private investment and encroachment on public space. They are being built by design at home, and supported by UK money abroad, with enormous expansion plans in India for example, in almost 100 cities.

With this rapid expansion of “smart” technology not only within our living rooms but my living space and indeed across all areas of life, how do we ensure equitable service delivery, (what citizens generally want, as demonstrated by strength of feeling on the NHS) continues in public ownership, when the boundary in current policy is ever more blurred between public and private corporate ownership?

How can we know and plan by-design that the values we hope for, are good values, and that they will be embedded in systems, in policies and planning? Values that most people really care about. How do we ensure “smart” does not ultimately mean less good? That “smart” does not in the end mean, less human.

Economic benefits seem to be the key driver in current government thinking around technology – more efficient = costs less.

While using technology progressing towards replacing repetitive work may be positive, how will we accommodate for those whose skills will no longer be needed? In particular its gendered aspect, and the more vulnerable in the workforce, since it is women and other minorities who work disproportionately in our part-time, low skill jobs. Jobs that are mainly held by women, even what we think of as intrinsically human, such as carers, are being trialed for outsourcing or assistance by technology. These robots monitor people, in their own homes and reduce staffing levels and care home occupancy. We’ll no doubt hear how good it is we need fewer carers because after all, we have a shortage of care staff. We’ll find out whether it is positive for the cared, or whether they find it it less ‘human'[e]. How will we measure those costs?

The ideal future of us all therefore having more leisure time sounds fab, but if we can’t afford it, we won’t be spending more of our time employed in leisure. Some think we’ll simply be unemployed. And more people live in the slums of Calcutta than in Soho.

One of the greatest benefits of technology is how more connected the world can be, but will it also be more equitable?

There are benefits in remote sensors monitoring changes in the atmosphere that dictate when cars should be taken off the roads on smog-days, or indicators when asthma risk-factors are high.

Crowd sourcing information about things which are broken, like fix-my-street, or lifts out-of-order are invaluable in cities for wheelchair users.

Innovative thinking and building things through technology can create things which solve simple problems and add value to the person using the tool.

But what of the people that cannot afford data, cannot be included in the skilled workforce, or will not navigate apps on a phone?

How this dis-incentivises the person using the technology has not only an effect on their disappointment with the tool, but the service delivery, and potentially wider still even to societal exclusion or stigma.These were the findings of the e-red book in Glasgow explained at the Digital event in health, held at the King’s Fund in summer 2015.

Further along the scale of systems and potential for negative user experience, how do we expect citizens to react to finding punishments handed out by unseen monitoring systems, finding out our behaviour was ‘nudged’ or find decisions taken about us, without us?

And what is the oversight and system of redress for people using systems, or whose data are used but inaccurate in a system, and cause injustice?

And wider still, while we encourage big money spent on big data in our part of the world how is it contributing to solving problems for millions for whom they will never matter? Digital and social media makes increasingly transparent our one connected world, with even less excuse for closing our eyes.

Approximately 15 million girls worldwide are married each year – that’s one girl, aged under 18, married off against her will every two seconds. [Huff Post, 2015]

Tinder-type apps are luxury optional extras for many in the world.

Without embedding values and oversight into some of what we do through digital tools implemented by private corporations for profit, ‘smart’ could mean less fair, less inclusive, less kind. Less global.

If digital becomes a destination, and how much it is implemented is seen as a measure of success, by measuring how “smart” we become risks losing sight of seeing technology as solutions and steps towards solving real problems for real people.

We need to be both clever and sensible, in our ‘smart’.

Are public oversight and regulation built in to make ‘smart’ also be safe?

If there were public consultation on how “smart” society will look would we all agree if and how we want it?

Thinking globally, we need to ask if we are prioritising the wrong problems? Are we creating more tech that we already have invented solutions for place where governments are willing to spend on them? And will it in those places make the society more connected across class and improve it for all, or enhance the lives of the ‘haves’ by having more, and the ‘have-nots’ be excluded?

Does it matter how smart your TV gets, or carer, or car, if you cannot afford any of these convenient add-ons to Life v1.1?

As we are ever more connected, we are a global society, and being ‘smart’ in one area may be reckless if at the expense or ignorance of another.

People need to Understand what “Smart” means

“Consistent with the wider global discourse on ‘smart’ cities, in India urban problems are constructed in specific ways to facilitate the adoption of “smart hi-tech solutions”. ‘Smart’ is thus likely to mean technocratic and centralized, undergirded by alliances between the Indian government and hi-technology corporations.”  [Saurabh Arora, Senior Lecturer in Technology and Innovation for Development at SPRU]

Those investing in both countries are often the same large corporations. Very often, venture capitalists.

Systems designed and owned by private companies provide the information technology infrastructure that i:

the basis for providing essential services to residents. There are many technological platforms involved, including but not limited to automated sensor networks and data centres.’

What happens when the commercial and public interest conflict and who decides that they do?

Decision making, Mining and Value

Massive amounts of data generated are being mined for making predictions, decisions and influencing public policy: in effect using Big Data for research purposes.

Using population-wide datasets for social and economic research today, is done in safe settings, using deidentified data, in the public interest, and has independent analysis of the risks and benefits of projects as part of the data access process.

Each project goes before an ethics committee review to assess its considerations for privacy and not only if the project can be done, but should be done, before it comes for central review.

Similarly our smart-cities need ethics committee review assessing the privacy impact and potential of projects before commissioning or approving smart-technology. Not only assessing if they are they feasible, and that we ‘can’ do it, but ‘should’ we do it. Not only assessing the use of the data generated from the projects, but assessing the ethical and privacy implications of the technology implementation itself.

The Committee recommendations on Big Data recently proposed that a ‘Council of Data Ethics’ should be created to explicitly address these consent and trust issues head on. But how?

Unseen smart-technology continues to grow unchecked often taking root in the cracks between public-private partnerships.

We keep hearing about Big Data improving public services but that “public” data is often held by private companies. In fact our personal data for public administration has been widely outsourced to private companies of which we have little oversight.

We’re told we paid the price in terms of skills and are catching up.

But if we simply roll forward in first gear into the connected city that sees all, we may find we arrive at a destination that was neither designed nor desired by the majority.

We may find that the “revolution, not evolution”, hoped for in digital services will be of the unwanted kind if companies keep pushing more and more for more data without the individual’s consent and our collective public buy-in to decisions made about data use.

Having written all this, I’ve now read the Royal Statistical Society’s publication which eloquently summarises their recent work and thinking. But I wonder how we tie all this into practical application?

How we do governance and regulation is tied tightly into the practicality of public-private relationships but also into deciding what should society look like? That is what our collective and policy decisions about what smart-cities should be and may do, is ultimately defining.

I don’t think we are addressing in depth yet the complexity of regulation and governance that will be sufficient to make Big Data and Public Spaces safe because companies say too much regulation risks choking off innovation and creativity.

But that risk must not be realised if it is managed well.

Rather we must see action to manage the application of smart-technology in a thoughtful way quickly, because if we do not, very soon, we’ll have lost any say in how our service providers deliver.

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I began my thoughts about this in Part one, on smart technology and data from the Sprint16 session and after this (Part two), continue to look at the design and development of smart technology making “The Best Use of Data” with a UK company case study (Part three) and “The Best Use of Data” used in predictions and the Future (Part four).

Destination smart-cities: design, desire and democracy (Part one)

When I drop my children at school in the morning I usually tell them three things: “Be kind. Have fun. Make good choices.”

I’ve been thinking recently about what a positive and sustainable future for them might look like. What will England be in 10 years?

The #Sprint16 snippets I read talk about how: ”Digital is changing how we deliver every part of government,” and “harnessing the best of digital and technology, and the best use of data to improve public services right across the board.”

From that three things jumped out at me:

  • The first is that the “best use of data” in government’s opinion may conflict with that of the citizen.
  • The second, is how to define “public services” right across the board in a world in which boundaries between private and public in the provision of services have become increasingly blurred.
  • And the third is the power of tech to offer both opportunity and risk if used in “every part of government” and effects on access to, involvement in, and the long-term future of, democracy.

What’s the story so far?

In my experience so far of trying to be a digital citizen “across the board” I’ve seen a few systems come and go. I still have my little floppy paper Government Gateway card, navy blue with yellow and white stripes. I suspect it is obsolete. I was a registered Healthspace user, and used it twice. It too, obsolete. I tested my GP online service. It was a mixed experience.

These user experiences are shaping how I interact with new platforms and my expectations of organisations, and I will be interested to see what the next iteration, nhs alpha, offers.

How platforms and organisations interact with me, and my data, is however increasingly assumed without consent. This involves new data collection, not only using data from administrative or commercial settings to which I have agreed, but new scooping of personal data all around us in “smart city” applications.

Just having these digital applications will be of no benefit and all the disadvantages of surveillance for its own sake will be realised.

So how do we know that all these data collected are used – and by whom? How do we ensure that all the tracking actually gets turned into knowledge about pedestrian and traffic workflow to make streets and roads safer and smoother in their operation, to make street lighting more efficient, or the environment better to breathe in and enjoy? And that we don’t just gift private providers tonnes of valuable data which they simply pass on to others for profit?

Because without making things better, in this Internet-of-Things will be a one-way ticket to power in the hands of providers and loss of control, and quality of life. We’ll work around it, but buying a separate SIM card for trips into London, avoiding certain parks or bridges, managing our FitBits to the nth degree under a pseudonym. But being left no choice but to opt out of places or the latest technology to enjoy, is also tedious. If we want to buy a smart TV to access films on demand, but don’t want it to pass surveillance or tracking information back to the company how can we find out with ease which products offer that choice?

Companies have taken private information that is none of their business, and quite literally, made it their business.

The consumer technology hijack of “smart” to always mean marketing surveillance creates a divide between those who will comply for convenience and pay the price in their privacy, and those who prize privacy highly enough to take steps that are less convenient, but less compromised.

But even wanting the latter, it can be so hard to find out how to do, that people feel powerless and give-in to the easy option on offer.

Today’s system of governance and oversight that manages how our personal data are processed by providers of public and private services we have today, in both public and private space, is insufficient to meet the values most people reasonably expect, to be able to live their life without interference.

We’re busy playing catch up with managing processing and use, when many people would like to be able to control collection.

The Best use of Data: Today

My experience of how the government wants to ‘best use data’ is that until 2013 I assumed the State was responsible with it.

I feel bitterly let down.

care.data taught me that the State thinks my personal data and privacy are something to exploit, and “the best use of my data” for them, may be quite at odds with what individuals expect. My trust in the use of my health data by government has been low ever since. Saying one thing and doing another, isn’t making it more trustworthy.

I found out in 2014 how my children’s personal data are commercially exploited and given to third parties including press outside safe settings, by the Department for Education. Now my trust is at rock bottom. I tried to take a look at what the National Pupil Database stores on my own children and was refused a subject access request, meanwhile the commercial sector and Fleet Street press are given out not only identifiable data, but ‘highly sensitive’ data. This just seems plain wrong in terms of security, transparency and respect for the person.

The attitude that there is an entitlement of the State to individuals’ personal data has to go.

The State has pinched 20 m children’s privacy without asking. Tut Tut indeed. [see Very British Problems for a translation].

And while I support the use of public administrative data in deidentified form in safe settings, it’s not to be expected that anything goes. But the feeling of entitlement to access our personal data for purposes other than that for which we consented, is growing, as it stretches to commercial sector data. However suggesting that public feeling measured based on work with 0.0001% of the population, is “wide public support for the use and re-use of private sector data for social research” seems tenuous.

Even so, comments even in that tiny population suggested, “many participants were taken by surprise at the extent and size of data collection by the private sector” and some “felt that such data capture was frequently unwarranted.” “The principal concerns about the private sector stem from the sheer volume of data collected with and without consent from individuals and the profits being made from linking data and selling data sets.”

The Best use of Data: The Future

Young people, despite seniors often saying “they don’t care about privacy” are leaving social media in search of greater privacy.

These things cannot be ignored if the call for digital transformation between the State and the citizen is genuine because try and do it to us and it will fail. Change must be done with us. And ethically.

And not “ethics” as in ‘how to’, but ethics of “should we.” Qualified transparent evaluation as done in other research areas, not an add on, but integral to every project, to look at issues such as:

  • whether participation is voluntary, opt-out or covert
  • how participants can get and give informed consent
  • accessibility to information about the collection and its use
  • small numbers, particularly of vulnerable people included
  • identifiable data collection or disclosure
  • arrangements for dealing with disclosures of harm and recourse
  • and how the population that will bear the risks of participating in the research is likely to benefit from the knowledge derived from the research or not.

Ethics is not about getting away with using personal data in ways that won’t get caught or hauled over the coals by civil society.

It’s balancing risk and benefit in the public interest, and not always favouring the majority, but doing what is right and fair.

We hear a lot at the moment on how the government may see lives, shaped by digital skills, but too little of heir vison for what living will look and feel like, in smart cities of the future.

My starting question is, how does government hope society will live there and is it up to them to design it? If not, who is because these smart-city systems are not designing themselves. You’ve heard of Stepford wives. I wonder what do we do if we do not want to live like Milton Keynes man?

I hope that the world my children will inherit will be more just, more inclusive, and with a more sustainable climate to support food, livelihoods and kinder than it is today. Will ‘smart’ help or hinder?

What is rarely discussed in technology discussions is how the service should look regardless of technology. The technology assumed as inevitable, becomes the centre of service delivery.

I’d like to first understand what is the central and local government vision for “public services”  provision for people of the future? What does it mean for everyday services like schools and health, and how does it balance security and our freedoms?

Because without thinking about how and who provides those services for people, there is a hole in the discussion of “the best use of data” and their improvement “right across the board”.

The UK government has big plans for big data sharing, sharing across all public bodies, some tailored for individual interventions.

While there are interesting opportunities for public benefit from at-scale systems, the public benefit is at risk not only from lack of trust in how systems gather data and use them, but that interoperability in service, and the freedom for citizens to transfer provider, gets lost in market competition.

Openness and transparency can be absent in public-private partnerships until things go wrong. Given the scale of smart-cities, we must have more than hope that data management and security will not be one of those things.

How will we know if new plans are designed well, or not?

When I look at my children’s future and how our current government digital decision making may affect it, I wonder if their future will be more or less kind. More or less fun.

Will they be left with the autonomy to make good choices of their own?

The hassle we feel when we feel watched all the time, by every thing that we own, in every place we go, having to check every check box has a reasonable privacy setting, has a cumulative cost in our time and anxieties.

Smart technology has invaded not only our public space and our private space, but has nudged into our head space.

I for one have had enough already. For my kids I want better. Technology should mean progress for people, not tyranny.

Living in smart cities, connected in the Internet-of-Things, run on their collective Big Data and paid for by commercial corporate providers, threatens not only their private lives and well-being, their individual and independent lives, but ultimately independent and democratic government as we know it.

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This is the start of a four part set of thoughts: Beginnings with smart technology and data triggered by the Sprint16 session (part one). I think about this more in depth in “Smart systems and Public Services” (Part two) here, and the design and development of smart technology making “The Best Use of Data” looking at today in a UK company case study (Part three) before thoughts on “The Best Use of Data” used in predictions and the Future (Part four).

Breaking up is hard to do. Restructuring education in England.

This Valentine’s I was thinking about the restructuring of education in England and its wide ranging effects. It’s all about the break up.

The US EdTech market is very keen to break into the UK, and our front door is open.

We have adopted the model of Teach First partnered with Teach America, while some worry we do not ask “What is education for?

Now we hear the next chair of Oftsed is to be sought from the US, someone who is renowned as “the scourge of the unions.”

Should we wonder how long until the management of schools themselves is US-sourced?

The education system in England has been broken up in recent years into manageable parcels  – for private organisations, schools within schools, charity arms of commercial companies, and multi-school chains to take over – in effect, recent governments have made reforms that have dismantled state education as I knew it.

Just as the future vision of education outlined in the 2005 Direct Democracy co-authored by Michael Gove said, “The first thing to do is to make existing state schools genuinely independent of the state.”

Free schools touted as giving parents the ultimate in choice, are in effect another way to nod approval to the outsourcing of the state, into private hands, and into big chains. Despite seeing the model fail spectacularly abroad, the government seems set on the same here.

Academies, the route that finagles private corporations into running public-education is the preferred model, says Mr Cameron. While there are no plans to force schools to become academies, the legislation currently in ping-pong under the theme of coasting schools enables just that. The Secretary of State can impose academisation. Albeit only on Ofsted labeled ‘failing’ schools.

What fails appears sometimes to be a school that staff and parents cannot understand as anything less than good, but small. While small can be what parents want, small pupil-teacher ratios, mean higher pupil-per teacher costs. But the direction of growth is towards ‘big’ is better’.

“There are now 87 primary schools with more than 800 pupils, up from 77 in 2014 and 58 in 2013. The number of infants in classes above the limit of 30 pupils has increased again – with 100,800 pupils in these over-sized classes, an increase of 8% compared with 2014.” [BBC]

All this restructuring creates costs about which the Department wants to be less than transparent.  And has lost track of.

If only we could see that these new structures raised standards?  But,” while some chains have clearly raised attainment, others achieve worse outcomes creating huge disparities within the academy sector.”

If not delivering better results for children, then what is the goal?

A Valentine’s view of Public Service Delivery: the Big Break up

Breaking up the State system, once perhaps unthinkable is possible through the creation of ‘acceptable’ public-private partnerships (as opposed to outright privatisation per se). Schools become academies through a range of providers and different pathways, at least to start with, and as they fail, the most successful become the market leaders in an oligopoly. Ultimately perhaps, this could become a near monopoly. Delivering ‘better’. Perhaps a new model, a new beginning, a new provider offering salvation from the flood of ‘failing’ schools coming to the State’s rescue.

In order to achieve this entry to the market by outsiders, you must first remove conditions seen as restrictive, giving more ‘freedom’ to providers; to cut corners make efficiency savings on things like food standards, required curriculum, and numbers of staff, or their pay.

And what if, as a result, staff leave, or are hard to recruit?

Convincing people that “tech” and “digital” will deliver cash savings and teach required skills through educational machine learning is key if staff costs are to be reduced, which in times of austerity and if all else has been cut, is the only budget left to slash.

Self-taught systems’ providers are convincing in their arguments that tech is the solution.

Sadly I remember when a similar thing was tried on paper. My first year of GCSE maths aged 13-14  was ‘taught’ at our secondary comp by working through booklets in a series that we self-selected from the workbench in the classroom. Then we picked up the master marking-copy once done. Many of the boys didn’t need long to work out the first step was an unnecessary waste of time. The teacher had no role in the classroom. We were bored to bits. By the final week at end of the year they sellotaped the teacher to his chair.

I kid you not.

Teachers are so much more than knowledge transfer tools, and yet by some today seem to be considered replaceable by technology.

The US is ahead of us in this model, which has grown hand-in-hand with commercialism in schools. Many parents are unhappy.

So is the DfE setting us up for future heartbreak if it wants us to go down the US route of more MOOCs, more tech, and less funding and fewer staff? Where’s the cost benefit risk analysis and transparency?

We risk losing the best of what is human from the classroom, if we will remove the values they model and inspire. Unions and teachers and educationalists are I am sure, more than aware of all these cumulative changes. However the wider public seems little engaged.

For anyone ‘in education’ these changes will all be self-evident and their balance of risks and benefits a matter of experience, and political persuasion. As a parent I’ve only come to understand these changes, through researching how our pupils’ personal and school data have been commercialised,  given away from the National Pupil Database without our consent, since legislation changed in 2013; and the Higher Education student and staff data sold.

Will more legislative change be needed to keep our private data accessible in public services operating in an increasingly privately-run delivery model? And who will oversee that?

The Education Market is sometimes referred to as ‘The Wild West’. Is it getting a sheriff?

The news that the next chair of Oftsed is to be sought from the US did set alarm bells ringing for some in the press, who fear US standards and US-led organisations in British schools.

“The scourge of unions” means not supportive of staff-based power and in health our junior doctors have clocked exactly what breaking their ‘union’ bargaining power is all about.  So who is driving all this change in education today?

Some ed providers might be seen as profiting individuals from the State break up. Some were accused of ‘questionable practices‘. Oversight has been lacking others said. Margaret Hodge in 2014 was reported to have said: “It is just wrong to hand money to a company in which you have a financial interest if you are a trustee.”

I wonder if she has an opinion on a lead non-executive board member at the Department for Education also being the director of one of the biggest school chains? Or the ex Minister now employed by the same chain? Or that his campaign was funded by the same Director?  Why this register of interests is not transparent is a wonder.

It could appear to an outsider that the private-public revolving door is well oiled with sweetheart deals.

Are the reforms begun by Mr Gove simply to be executed until their end goal, whatever that may be, through Nikky Morgan or she driving her own new policies?

If Ofsted were  to become US-experience led, will the Wild West be tamed or US providers invited to join the action, reshaping a new frontier? What is the end game?

Breaking up is not hard to do, but in whose best interest is it?

We need only look to health to see the similar pattern.

The structures are freed up, and boundaries opened up (if you make the other criteria) in the name of ‘choice’. The organisational barriers to break up are removed in the name of ‘direct accountability’. And enabling plans through more ‘business intelligence’ gathered from data sharing, well, those plans abound.

Done well, new efficient systems and structures might bring public benefits, the right technology can certainly bring great things, but have we first understood what made the old less efficient if indeed it was and where are those baselines to look back on?

Where is the transparency of the end goal and what’s the price the Department is prepared to pay in order to reach it?

Is reform in education, transparent in its ideology and how its success is being measured if not by improved attainment?

The results of change can also be damaging. In health we see failing systems and staff shortages and their knock-on effects into patient care. In schools, these failures damage children’s start in life, it’s not just a ‘system’.

Can we assess if and how these reforms are changing the right things for the right reasons? Where is the transparency of what problems we are trying to solve, to assess what solutions work?

How is change impact for good and bad being measured, with what values embedded, with what oversight, and with whose best interests at its heart?

2005’s Direct Democracy could be read as a blueprint for co-author Mr Gove’s education reforms less than a decade later.

Debate over the restructuring of education and its marketisation seems to have bypassed most of us in the public, in a way health has not.

Underperformance as measured by new and often hard to discern criteria, means takeover at unprecedented pace.

And what does this mean for our most vulnerable children? SEN children are not required to be offered places by academies. The 2005 plans co-authored by Mr Gove also included: “killing the government’s inclusion policy stone dead,” without an alternative.

Is this the direction of travel our teachers and society supports?

What happens when breakups happen and relationship goals fail?

Who picks up the pieces? I fear the state is paying heavily for the break up deals, investing heavily in new relationships, and yet will pay again for failure. And so will our teaching staff, and children.

While Mr Hunt is taking all the heat right now, for his part in writing Direct Democracy and its proposals to privatise health – set against the current health reforms and restructuring of junior doctors contracts – we should perhaps also look to Mr Gove co-author, and ask to better understand the current impact of his recent education reforms, compare them with what he proposed in 2005, and prepare for the expected outcomes of change before it happens (see p74).

One outcome was that failure was to be encouraged in this new system, and Sweden held up as an exemplary model:

“Liberating state schools would also allow the all-important freedom to fail.”

As Anita Kettunen, principal of JB Akersberga in Sweden reportedly said when the free schools chain funded by a private equity firm failed:

“if you’re going to have a system where you have a market, you have to be ready for this.”

Breaking up can be hard to do. Failure hurts. Are we ready for this?
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Abbreviated on Feb 18th.