care.data, change management, datasharing, engagement, ethics, HSCIC, human rights, NHS England, politics, privacy

A vanquished ghost returns as details of distress required in NHS opt out

It seems the ugly ghosts of care.data past were alive and well at NHS Digital this Christmas.

Old style thinking, the top-down patriarchal ‘no one who uses a public service should be allowed to opt out of sharing their records. Nor can people rely on their record being anonymised,‘ that you thought was vanquished, has returned with a vengeance.

The Secretary of State for Health, Jeremy Hunt, has reportedly  done a U-turn on opt out of the transfer of our medical records to third parties without consent.

That backtracks on what he said in Parliament on January 25th, 2014 on opt out of anonymous data transfers, despite the right to object in the NHS constitution [1].

So what’s the solution? If the new opt out methods aren’t working, then back to the old ones and making Section 10 requests? But it seems the Information Centre isn’t keen on making that work either.

All the data the HSCIC holds is sensitive and as such, its release risks patients’ significant harm or distress [2] so it shouldn’t be difficult to tell them to cease and desist, when it comes to data about you.

But how is NHS Digital responding to people who make the effort to write directly?

Someone who “got a very unhelpful reply” is being made to jump through hoops.

If anyone asks that their hospital data should not be used in any format and passed to third parties, that’s surely for them to decide.

Let’s take the case study of a woman who spoke to me during the whole care.data debacle who had been let down by the records system after rape. Her NHS records subsequently about her mental health care were inaccurate, and had led to her being denied the benefit of private health insurance at a new job.

Would she have to detail why selling her medical records would cause her distress? What level of detail is fair and who decides? The whole point is, you want to keep info confidential.

Should you have to state what you fear? “I have future distress, what you might do to me?” Once you lose control of data, it’s gone. Based on past planning secrecy and ideas for the future, like mashing up health data with retail loyalty cards as suggested at Strata in November 2013 [from 16:00] [2] no wonder people are sceptical. 

Given the long list of commercial companies,  charities, think tanks and others that passing out our sensitive data puts at risk and given the Information Centre’s past record, HSCIC might be grateful they have only opt out requests to deal with, and not millions of medical ethics court summonses. So far.

HSCIC / NHS Digital has extracted our identifiable records and has given them away, including for commercial product use, and continues give them away, without informing us. We’ve accepted Ministers’ statements and that a solution would be found. Two years on, patience wears thin.

“Without that external trust, we risk losing our public mandate and then cannot offer the vital insights that quality healthcare requires.”

— Sir Nick Partridge on publication of the audit report of 10% of 3,059 releases by the HSCIC between 2005-13

— Andy WIlliams said, “We want people to be certain their choices will be followed.”

Jeremy Hunt said everyone should be able to opt out of having their anonymised data used. David Cameron did too when the plan was  announced in 2012.

In 2014 the public was told there should be no more surprises. This latest response is not only a surprise but enormously disrespectful.

When you’re trying to rebuild trust, assuming that we accept that ‘is’ the aim, you can’t say one thing, and do another.  Perhaps the Department for Health doesn’t like the public answer to what the public wants from opt out, but that doesn’t make the DH view right.

Perhaps NHS Digital doesn’t want to deal with lots of individual opt out requests, that doesn’t make their refusal right.

Kingsley Manning recognised in July 2014, that the Information Centre “had made big mistakes over the last 10 years.” And there was “a once-in-a-generation chance to get it right.”

I didn’t think I’d have to move into the next one before they fix it.

The recent round of 2016 public feedback was the same as care.data 1.0. Respect nuanced opt outs and you will have all the identifiable public interest research data you want. Solutions must be better for other uses, opt out requests must be respected without distressing patients further in the process, and anonymous must mean  anonymous.

“Pseudonymised data requests that go through the DARS process so that a Data Sharing Framework Contract and Data Sharing Agreement are in place are considered to be compliant with the ICO code of practice“ – fine, but they are not anonymous. If DARS is still giving my family’s data to Experian, Harvey Walsh, and co, despite opt out, I’ll be furious.

The [Caldicott 2] Review Panel found “that commissioners do not need dispensation from confidentiality, human rights & data protection law.”

Neither do our politicians, their policies or ALBs.

[1] https://www.england.nhs.uk/ourwork/tsd/ig/ig-fair-process/further-info-gps/

“A patient can object to their confidential personal information from being disclosed out of the GP Practice and/or from being shared onwards by the HSCIC for non-direct care purposes (secondary purposes).”

[2] Minimum Mandatory Measures http://www.nationalarchives.gov.uk/documents/information-management/cross-govt-actions.pdf p7

politics, privacy, transparency, trust

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.

#caredata, AI, care.data, change

DeepMind or DeepMined? NHS public data, engagement and regulation repackaged

A duty of confidentiality and the regulation of medical records are as old as the hills. Public engagement on attitudes in this in context of the NHS has been done and published by established social science and health organisations in the last three years. So why is Google DeepMind (GDM) talking about it as if it’s something new? What might assumed consent NHS-wide mean in this new context of engagement? Given the side effects for public health and medical ethics of a step-change towards assumed consent in a commercial product environment, is this ‘don’t be evil’ shift to ‘do no harm’ good enough?  Has Regulation failed patients?
My view from the GDM patient and public event, September 20.

Involving public and patients

Around a hundred participants joined the Google DeepMind public and patient event,  in September after which Paul Wicks gave his view in the BMJ afterwards, and rightly started with the fact the event was held in the aftermath of some difficult questions.

Surprisingly, none were addressed in the event presentations. No one mentioned data processing failings, the hospital Trust’s duty of confidentiality, or criticisms in the press earlier this year. No one talked about the 5 years of past data from across the whole hospital or monthly extracts that were being shared and had first been extracted for GDM use without consent.

I was truly taken aback by the sense of entitlement that came across. The decision by the Trust to give away confidential patient records without consent earlier in 2015/16 was either forgotten or ignored and until the opportunity for questions,  the future model was presented unquestioningly. The model for an NHS-wide hand held gateway to your records that the announcement this week embeds.

What matters on reflection is that the overall reaction to this ‘engagement’ is bigger than the one event, bigger than the concepts of tools they could hypothetically consider designing, or lack of consent for the data already used.

It’s a massive question of principle, a litmus test for future commercial users of big, even national population-wide public datasets.

Who gets a say in how our public data are used? Will the autonomy of the individual be ignored as standard, assumed unless you opt out, and asked for forgiveness with a post-haste opt out tacked on?

Should patients just expect any hospital can now hand over all our medical histories in a free-for-all to commercial companies and their product development without asking us first?

Public and patient questions

Where data may have been used in the algorithms of the DeepMind black box, there was a black hole in addressing patient consent.

Public engagement with those who are keen to be involved, is not a replacement for individual permission from those who don’t want to be, and who expected a duty of patient-clinician confidentiality.

Tellingly, the final part of the event tried to be a capture our opinions on how to involve the public. Right off the bat the first question was one of privacy. Most asked questions about issues raised to date, rather than looking to design the future. Ignoring those and retrofitting a one-size fits all model under the banner of ‘engagement’ won’t work until they address concerns of those people they have already used and the breach of trust that now jeopardises people’s future willingness to be involved, not only in this project, but potentially other research.

This event should have been a learning event for Google which is good at learning and uses people to do it both by man and machine.

But from their post-media reaction after  this week’s announcement it seems not all feedback or lessons learned are welcome.

Google DeepMind executives were keen to use patient case studies and had patients themselves do the most talking, saying how important data is to treat kidney and eyecare, which I respect greatly. But there was very little apparent link why their experience was related to Google DeepMind at all or products created to date.

Google DeepMind has the data from every patient in the hospital in recent years, not only patients affected by this condition and not data from the people who will be supported directly by this app.

Yet GoogleDeepMind say this is “direct care” not research. Hard to be for direct care when you are no longer under the hospital’s care. Implied consent for use of sensitive health data, needs to be used in alignment with the purposes for which it was given. It must be fair and lawful.

If data users don’t get that, or won’t accept it, they should get out of healthcare and our public data right now. Or heed advice of critical friends and get it right to be trustworthy in future. .

What’s the plan ahead?

Beneath the packaging, this came across as a pitch on why Google DeepMind should get access to paid-for-by-the-taxpayer NHS patient data. They have no clinical background or duty of care. They say they want people to be part of a rigorous process, including a public/patient panel, but it’s a process they clearly want to shape and control, and for a future commercial model. Can a public panel be truly independent, and ethical, if profit plays a role?

Of course it’s rightly exciting for healthcare to see innovation and drives towards better clinical care, but not only the intent but how it gets done matters. This matters because it’s not a one-off.

The anticipation in the room of ‘if only we could access the whole NHS data cohort’ was tangible in the room, and what a gift it would be to commercial companies and product makers. Wrapped in heart wrenching stories. Stories of real-patients, with real-lives who genuinely want improvement for all. Who doesn’t want that? But hanging on the coat tails of Mr Suleyman were a range of commmercial companies and third party orgs asking for the same.

In order to deliver those benefits and avoid its risks there is well-established framework of regulation and oversight of UK  practitioners and use of medical records and in medical devices and tools: the General Medical Council, the Health and Social Care Information Centre (Now called ‘NHS Digital’), Confidentiality Advisory Group (CAG)and more, all have roles to play.

Google DeepMind and the Trusts have stepped outwith that framework and been playing catch up not only with public involvement, but also with MHRA regulatory approval.

One of the major questions is around the invisibility of data science decisions that have direct interventions in people’s life and death.

The ethics of data sciences in which decisions are automated, requires us to “guard against dangerous assumptions that algorithms are near-perfect, or more perfect than human judgement.”  (The Opportunities and Ethics of Big Data. [1])

If Google DeepMind now plans to share their API widely who will proof their tech? Who else gets to develop something similar?

Don’t be evil 2.0

Google DeepMind appropriated ‘do no harm’ as the health event motto, echoing the once favored Google motto ‘don’t be evil’.

However, they really needed to address that the fragility of some patients’ trust in their clinicians has been harmed already, before DeepMind has even run an algorithm on the data, simply because patient data was given away without patients’ permission.

A former Royal Free patient spoke to me at the event and said they were shocked to have to have first read in the papers that their confidential medical records had been given to Google without their knowledge. Another said his mother had been part of the cohort and has concerns. Why weren’t they properly informed? The public engagement work they should to my mind be doing, is with the London hospital individual patients whose data they have already been using without their consent, explaining why they got their confidential medical records without telling them, and addressing their questions and real concerns. Not at a flash public event.

I often think in the name, they just left off the ‘e’. They are Google. We are the deep mined. That may sound flippant but it’s not the intent. It’s entirely serious. Past patient data was handed over to mine, in order to think about building a potential future tool.

There was a lot of if, future, ambition, and sweeping generalisations and ‘high-level sketches’ of what might be one day. You need moonshots to boost discovery, but losing patient trust even of a few people, cannot be a casualty we should casually accept. For the company there is no side effect. For patients, it could last a lifetime.

If you go back to the roots of health care, you could take the since misappropriated Hippocratic Oath and quote not only, as Suleyman did, “do no harm” , but the next part. “I will not play God.”

Patriarchal top down Care.data was a disastrous model of engagement that confused communication with ‘tell the public loudly and often what we want to happen, what we think best, and then disregard public opinion.’ A model that doesn’t work.

The recent public engagement event on the National Data Guardian work consent models certainly appear from the talks to be learning those lessons. To get it wrong in commercial use, will be disastrous.

The far greater risk from this misadventure is not company  reputation, which seems to be top among Google DeepMind’s greatest concern. The risk that Google DeepMind seems prepared to take is one that is not at its cost, but that of public trust in the hospitals and NHS brand, public health, and its research.

Commercial misappropriation of patient data without consent could set back restoration of public trust and work towards a better model that has been work-in-progress since care.data car crash of 2013.

You might be able to abdicate responsibility if you think you’re not the driver. But where does the buck stop for contributory failure?

All this, says Google DeepMind, is nothing new, but Google isn’t other companies and this is a massive pilot move by a corporate giant into first appropriating and then brokering access to NHS-wide data to make an as-yet opaque private profit.  And being paid by the hospital trust to do so. Creating a data-sharing access infrastructure for the Royal Free is product development and one that had no permission to use 5 years worth of patient records to do so.

The care.data catastrophe may have damaged public trust and data access for public interest research for some time, but it did so doing commercial interests a massive favour. An assumption of ‘opt out’ rather than ‘opt in’ has become the NHS model. If the boundaries are changing of what is assumed under that, do the public still have no say in whether that is satisfactory? Because it’s not.

This example should highlight why an opt out model of NHS patient data is entirely unsatisfactory and cannot continue for these uses.

Should boundaries be in place?

So should boundaries in place in the NHS before this spreads. Hell yes. If as Mustafa said, it’s not just about developing technology but the process, regulatory and governance landscapes, then we should be told why their existing use of patient data intended for the Streams app development steam-rollered through those existing legal and ethical landscapes we have today. Those frameworks exist to preserve patients from quacks and skullduggery.

This then becomes about the duty of the controller and rights of the patient. It comes back to what we release, not only how it is used.

Can a panel of highly respected individuals intervene to embed good ethics if plans conflict with the purpose of making money from patients? Where are the boundaries between private and public good? Where they quash consent, where are its limitations and who decides? What boundaries do hospital trusts think they have on the duty of confidentiality?

It is for the hospitals as the data controllers from information received through their clinicians that responsibility lies.

What is next for Trusts? Giving an entire hospital patient database to supermarket pharmacies, because they too might make a useful tool? Mash up your health data with your loyalty card? All under assumed consent because product development is “direct care” because it’s clearly not research? Ethically it must be opt in.

App development is not using data for direct care. It is in product development. Post-truth packaging won’t fly. Dressing up the donkey by simply calling it by another name, won’t transform it into a unicorn, no matter how much you want to believe in it.

“In some sense I recognise that we’re an exceptional company, in other senses I think it’s important to put that in the wider context and focus on the patient benefit that we’re obviously trying to deliver.” [TechCrunch, November 22]

We’ve heard the cry, to focus on the benefit before. Right before care.data  failed to communicate to 50m people what it was doing with their health records. Why does Google think they’re different? They don’t. They’re just another company normalising this they say.

The hospitals meanwhile, have been very quiet.

What do patients want?

This was what Google DeepMind wanted to hear in the final 30 minutes of the event, but didn’t get to hear as all the questions were about what have you done so far and why?

There is already plenty of evidence what the public wants on the use of their medical records, from public engagement work that has already been done around NHS health data use from workshops and surveys since 2013. Public opinion is pretty clear. Many say companies should not get NHS records for commercial exploitation without consent at all (in the ESRC public dialogues on data in 2013, the Royal Statistical Society’s data trust deficit with lessons for policy makers work with Ipsos MORI in 2014, and the Wellcome Trust one-way mirror work in 2016 as well of course as the NHS England care.data public engagement workshops in 2014).

mirror

All those surveys and workshops show the public have consistent levels of concern about having a lack of control over who has access to their NHS data for what purposes and unlimited scope or future, and commercial purposes of their data is a red-line for many people.

A red-line which this Royal Free Google DeepMind project appeared to want to wipe out as if it had never been drawn at all.

I am sceptical that Google DeepMind has not done their research into existing public opinion on health data uses and research.

Those studies in public engagement already done by leading health and social science bodies state clearly that commercial use is a red line for some.

So why did they cross it without consent? Tell me why I should trust the hospitals to get this right with this company but trust you not to get it wrong with others. Because Google’s the good guys?

If this event and thinking ‘let’s get patients to front our drive towards getting more data’ sought to legitimise what they and these London hospitals are already getting wrong, I’m not sure that just ‘because we’re Google’ being big, bold and famous for creative disruption, is enough. This is a different game afoot. It will be a game-changer for patient rights to privacy if this scale of commercial product exploitation of identifiable NHS data becomes the norm at a local level to decide at will. No matter how terrific the patient benefit should be, hospitals can’t override patient rights.

If this steamrollers over consent and regulations, what next?

Regulation revolutionised, reframed or overruled

The invited speaker from Patients4Data spoke in favour of commercial exploitation as a benefit for the NHS but as Paul Wicks noted, was ‘perplexed as to why “a doctor is worried about crossing the I’s and dotting the T’s for 12 months (of regulatory approval)”.’

Appropriating public engagement is one thing. Appropriating what is seen as acceptable governance and oversight is another. If a new accepted model of regulation comes from this, we can say goodbye to the old one.  Goodbye to guaranteed patient confidentiality. Goodbye to assuming your health data are not open to commercial use.  Hello to assuming opt out of that use is good enough instead.

Trusted public regulatory and oversight frameworks exist for a reason. But they lag behind the industry and what some are doing. And if big players can find no retribution in skipping around them and then being approved in hindsight there’s not much incentive to follow the rules from the start. As TechCrunch suggested after the event, this is all “pretty standard playbook for tech firms seeking to workaround business barriers created by regulation.”

Should patients just expect any hospital can now hand over all our medical histories in a free-for-all to commercial companies without asking us first? It is for the Information Commissioner to decide whether the purposes of product design were what patients expected their data to be used for, when treated 5 years ago.

The state needs to catch up fast. The next private appropriation of the regulation of  AI collaboration oversight, has just begun. Until then, I believe civil society will not be ‘pedalling’ anything, but I hope will challenge companies cheek by jowl in any race to exploit personal confidential data and universal rights to privacy [2] by redesigning regulation on company terms.

Let’s be clear. It’s not direct care. It’s not research. It’s product development. For a product on which the commercial model is ‘I don’t know‘. How many companies enter a 5 year plan like that?

Benefit is great. But if you ignore the harm you are doing in real terms to real lives and only don’t see it because they’ve not talked to you, ask yourself why that is, not why you don’t believe it matters.

There should be no competition in what is right for patient care and data science and product development. The goals should be the same. Safe uses of personal data in ways the public expect, with no surprises. That means consent comes first in commercial markets.

[1] Olivia Varley-Winter, Hetan Shah, ‘The opportunities and ethics of big data: practical priorities for a national Council of Data Ethics.’ Theme issue ‘The ethical impact of data science’ compiled and edited by Mariarosaria Taddeo and Luciano Floridi. [The Royal Society, Volume 374, issue 2083]

[2] Universal rights to privacy: Upcoming Data Protection legislation (GDPR) already in place and enforceable from May 25, 2018 requires additional attention to fair processing, consent, the right to revoke it, to access one’s own and seek redress for inaccurate data. “The term “child” is not defined by the GDPR. Controllers should therefore be prepared to address these requirements in notices directed at teenagers and young adults.”

The Rights of the Child: Data policy and practice about children’s confidential data will impinge on principles set out in the United Nations Convention on the Rights of the Child, Article 12, the right to express views and be heard in decisions about them and Article 16 a right to privacy and respect for a child’s family and home life if these data will be used without consent. Similar rights that are included in the common law of confidentiality.

Article 8 of the Human Rights Act 1998 incorporating the European Convention on Human Rights Article 8.1 and 8.2 that there shall be no interference by a  public authority on the respect of private and family life that is neither necessary or proportionate.

Judgment of the Court of Justice of the European Union in the Bara case (C‑201/14) (October 2015) reiterated the need for public bodies to legally and fairly process personal data before transferring it between themselves. Trusts need to respect this also with contractors.

The EU Charter of Fundamental Rights, Article 52 also protects the rights of individuals about data and privacy and Article 52 protects the essence of these freedoms.

AI, engagement, ethics, politics, privacy

Data for Policy: Ten takeaways from the conference

The knowledge and thinking on changing technology, the understanding of the computing experts and those familiar with data, must not stay within conference rooms and paywalls.

What role do data and policy play in a world of post-truth politics and press? How will young people become better informed for their future?

The data for policy conference this week, brought together some of the leading names in academia and a range of technologists, government representatives, people from the European Commission, and other global organisations, Think Tanks, civil society groups, companies, and individuals interested in data and statistics. Beyond the UK, speakers came from several other countries in Europe, from the US, South America and Australia.

The schedule was ambitious and wide-ranging in topics. There was brilliant thinking and applications of ideas. Theoretical and methodological discussions were outnumbered by the presentations that included practical applications or work in real-life scenarios using social science data, humanitarian data, urban planning, public population-wide administrative data from health, finance, documenting sexual violence and more. This was good.

We heard about lots of opportunities and applied projects where large datasets are being used to improve the world. But while I always come away from these events having learned something and encouraged to learn more about those I didn’t, I do wonder if the biggest challenges in data and policy aren’t still the simplest.

No matter how much information we have, we must use it wisely. I’ve captured ten takeaways of things I would like to see follow. This may not have been the forum for it.

Ten takeaways on Data-for-Policy

1. Getting beyond the Bubble

All this knowledge must reach beyond the bubble of academia, beyond a select few white-male-experts-in well off parts of the world, and get into the hands and heads of the many. Ways to do this must include reducing the cost or changing  pathways of academic print access. Event and conference fees are also a  barrier to many.

2. Context of accessibility and control

There is little discussion of the importance of context. The nuance of most of these subjects was too much for the length of the sessions but I didn’t hear any single session mention threats to data access and trust in data collection posed by surveillance or state censorship or restriction of access to data or information systems, or the editorial control of knowledge and news by Facebook and co. There was no discussion of the influence of machine manipulators, how bots change news or numbers and create fictitious followings.

Policy makers and public are influenced by the media, post-truth or not. Policy makers in the UK government recently wrote in response to challenge over a Statutory Instrument that if Mums-net wasn’t kicking up  a fuss then they believed the majority of the public were happy. How are policy makers being influenced by press or social media statistics without oversight or regulating for their accuracy?

Increasing data and technology literacy in policy makers, is going to go far beyond improving an understanding of data science.

3. Them and Us

I feel a growing disconnect between those ‘in the know’ and those in ‘the public’. Perhaps that is a side-effect of my own understanding growing about how policy is made, but it goes wider. Those who talked about ‘the public’ did so without mention that attendees are all part of that public. Big data, are often our data. We are the public.

Vast parts of the population feel left behind already by policy and government decision-making; divided by income, Internet access, housing, life opportunites, and the ability to realise our dreams.

How policy makers address this gulf in the short and long term both matter as a foundation for what data infrastructure we have access to, how well trusted it is, whose data are included and who is left out of access to the information or decision-making using it.

Researchers prevented from accessing data held by government departments, perhaps who fear it will be used to criticise rather than help improve policy of the day, may be limiting our true picture of some of this divide and its solutions.

Equally data that is used to implement top-down policy without public involvement, seems a shame to ignore public opinion. I would like to have asked, does GDS in its land survey work searching for free school sites include people surveys asking, do you want a free school in your area at all?

4. There is no neutral

Global trust in politics is in tatters. Trust in the media is as bad. Neither appear to be interested across the world in doing much to restore their integrity.

All the wisdom in the world could not convince a majority in the 23rd June referendum, that the UK should remain in the European Union. This unspoken context was perhaps an aside to most of the subjects of the conference which went beyond the UK,  but we cannot ignore that the UK is deep in political crisis in the world, and at home the Opposition seems to have gone into a tailspin.

What role do data and evidence have in post-truth politics?

It was clear in discussion, that if I mentioned technology and policy in a political context, eyes started to glaze over. Politics should not interfere with the public interest, but it does and cannot be ignored. In fact it is short term political terms and needs for long term vision that are perhaps most at-odds in making good data policy plans.

The concept of public good, is not uncomplicated. It is made more complex still if you factor in changes over time, and cannot ignore that Trump or Turkey are not fictitious backdrops considering who decides what the public good and policy priorities should be.

Researchers’ role in shaping public good is not only about being ethical in their own research, but having the vision to have safeguards in place for how the knowledge they create are used.

5. Ethics is our problem, but who has the solution?

While many speakers touched on the common themes of ethics and privacy in data collection and analytics, saying this is going to be one of our greatest challenges, few address how, and who is taking responsibility and accountability for making it happen in ways that are not left to big business and profit making decision-takers.

It appears from last year, that ethics played a more central role. A year later we now have two new ethical bodies in the UK, at the UK Statistics Authority and at the Turing Institute. How they will influence the wider ethics issues in data science remains to be seen.

Legislation and policy are not keeping pace with the purchasing power or potential of the big players, the Googles and Amazons and Microsofts, and a government that sees anything resulting in economic growth as good, is unlikely to be willing to regulate it.

How technology can be used and how it should be used still seems a far off debate that no one is willing to take on and hold policy makers to account for. Implementing legislation and policy underpinned with ethics must serve as a framework for giving individuals insight into how decisions about them were reached by machines, or the imbalance of power that commercial companies and state agencies have in our lives that comes from insights through privacy invasion.

6. Inclusion and bias

Clearly this is one event in a world of many events that address similar themes, but I do hope that the unequal balance in representation across the many diverse aspects of being human are being addressed elsewhere.  A wider audience must be inclusive. The talk by Jim Waldo on retaining data accuracy while preserving privacy was interesting as it showed how deidentified data can create bias in results if data is very different from the original. Gaps in data, especially using big population data which excludes certain communities, wasn’t something I heard discussed as much.

7.Commercial data sources

Government and governmental organisations appear to be starting to give significant weight to the use of commercial data and social media data sources. I guess any data seen as ‘freely available’ that can be mined seems valuable. I wonder however how this will shape the picture of our populations, with what measures of validity and  whether data are comparable and offer reproducability.

These questions will matter in shaping policy and what governments know about the public. And equally, they must consider those communities whether in the UK or in other countries, that are not represented in these datasets and how these bias decision-making.

8. Data is not a panacea for policy making

Overall my take away is the important role that data scientists have to remind policy makers that data is only information. Nothing new. We may be able to access different sources of data in different ways, and process it faster or differently from the past, but we cannot rely on data of itself to solve the universal problems of the human condition. Data must be of good integrity to be useful and valuable. Data must be only one part of the library of resources to be used in planning policy. The limitations of data must also be understood. The uncertainties and unknowns can be just as important as evidence.

9. Trust and transparency

Regulation and oversight matter but cannot be the only solutions offered to concerns about shaping what is possible to do versus what should be done. Talking about protecting trust is not enough. Organisations must become more trustworthy if trust levels are to change; through better privacy policies, through secure data portability and rights to revoke consent and delete outdated data.

10. Young people and involvement in their future

What inspired me most were the younger attendees presenting posters, especially the PhD student using data to provide evidence of sexual violence in El Salvador and their passion for improving lives.

We are still not talking about how to protect and promote privacy in the Internet of Things, where sensors on every street corner in Smart Cities gather data about where we have been, what we buy and who we are with. Even our children’s toys send data to others.

I’m still as determined to convince policy makers that young people’s data privacy and digital self-awareness must be prioritised.

Highlighting the policy and practice failings in the niche area of the National Pupil Database serves only to get ideas from others how  policy and practice could be better. 20 million school children’s records is not a bad place to start to make data practice better.

The questions that seem hardest to move forward are the simplest: how to involve everyone in what data and policy may bring for future and not leave out certain communities through carelessness.

If the public is not encouraged to understand how our own personal data are collected and used, how can we expect to grow great data scientists of the future? What uses of data put good uses at risk?

And we must make sure we don’t miss other things, while data takes up the time and focus of today’s policy makers and great minds alike.

cb-poster-for-web

#caredata, care.data, change management, consent, datasharing, engagement, HSCIC, NHS, NHS England, privacy, trust

care.data listening events and consultation: The same notes again?

If lots of things get said in a programme of events, and nothing is left around to read about it, did they happen?

The care.data programme 2014-15 listening exercise and action plan has become impossible to find online. That’s OK, you might think, the programme has been scrapped. Not quite.

You can give your views online until September 7th on the new consultation, “New data security standards and opt-out models for health and social care”  and/or attend the new listening events, September 26th in London, October 3rd in Southampton and October 10th in Leeds.

The Ministerial statement on July 6, announced that NHS England had taken the decision to close the care.data programme after the review of data security and consent by Dame Fiona Caldicott, the National Data Guardian for Health and Care.

But the same questions are being asked again around consent and use of your medical data, from primary and secondary care. What a very long questionnaire asks is in effect,  do you want to keep your medical history private? You can answer only Q 15 if you want.

Ambiguity again surrounds what constitutes “de-identified” patient information.

What is clear is that public voice seems to have been deleted or lost from the care.data programme along with the feedback and brand.

People spoke up in 2014, and acted. The opt out that 1 in 45 people chose between January and March 2014 was put into effect by the HSCIC in April 2016. Now it seems, that might be revoked.

We’ve been here before.  There is no way that primary care data can be extracted without consent without it causing further disruption and damage to public trust and public interest research.  The future plans for linkage between all primary care data and secondary data and genomics for secondary uses, is untenable without consent.

Upcoming events cost time and money and will almost certainly go over the same ground that hours and hours were spent on in 2014. However if they do achieve a meaningful response rate, then I hope the results will not be lost and will be combined with those already captured under the ‘care.data listening events’ responses.  Will they have any impact on what consent model there may be in future?

So what we gonna do? I don’t know, whatcha wanna do? Let’s do something.

Let’s have accredited access and security fixed. While there may now be a higher transparency and process around release, there are still problems about who gets data and what they do with it.

Let’s have clear future scope and control. There is still no plan to give the public rights to control or delete data if we change our minds who can have it or for what purposes. And that is very uncertain. After all, they might decide to privatise or outsource the whole thing as was planned for the CSUs. 

Let’s have answers to everything already asked but unknown. The questions in the previous Caldicott review have still to be answered.

We have the possibility to  see health data used wisely, safely, and with public trust. But we seem stuck with the same notes again. And the public seem to be the last to be invited to participate and views once gathered, seem to be disregarded. I hope to be proved wrong.

Might, perhaps, the consultation deliver the nuanced consent model discussed at public listening exercises that many asked for?

Will the care.data listening events feedback summary be found, and will its 2014 conclusions and the enacted opt out be ignored? Will the new listening event view make more difference than in 2014?

Is public engagement, engagement, if nobody hears what was said?

AI, change management, empowerment, engagement, ethics, surveillance, Tech, transparency

Mum, are we there yet? Why should AI care.

Mike Loukides drew similarities between the current status of AI and children’s learning in an article I read this week.

The children I know are always curious to know where they are going, how long will it take, and how they will know when they get there. They ask others for guidance often.

Loukides wrote that if you look carefully at how humans learn, you see surprisingly little unsupervised learning.

If unsupervised learning is a prerequisite for general intelligence, but not the substance, what should we be looking for, he asked. It made me wonder is it also true that general intelligence is a prerequisite for unsupervised learning? And if so, what level of learning must AI achieve before it is capable of recursive self-improvement? What is AI being encouraged to look for as it learns, what is it learning as it looks?

What is AI looking for and how will it know when it gets there?

Loukides says he can imagine a toddler learning some rudiments of counting and addition on his or her own, but can’t imagine a child developing any sort of higher mathematics without a teacher.

I suggest a different starting point. I think children develop on their own, given a foundation. And if the foundation is accompanied by a purpose — to understand why they should learn to count, and why they should want to — and if they have the inspiration, incentive and  assets they’ll soon go off on their own, and outstrip your level of knowledge. That may or may not be with a teacher depending on what is available, cost, and how far they get compared with what they want to achieve.

It’s hard to learn something from scratch by yourself if you have no boundaries to set knowledge within and search for more, or to know when to stop when you have found it.

You’ve only to start an online course, get stuck, and try to find the solution through a search engine to know how hard it can be to find the answer if you don’t know what you’re looking for. You can’t type in search terms if you don’t know the right words to describe the problem.

I described this recently to a fellow codebar-goer, more experienced than me, and she pointed out something much better to me. Don’t search for the solution or describe what you’re trying to do, ask the search engine to find others with the same error message.

In effect she said, your search is wrong. Google knows the answer, but can’t tell you what you want to know, if you don’t ask it in the way it expects.

So what will AI expect from people and will it care if we dont know how to interrelate? How does AI best serve humankind and defined by whose point-of-view? Will AI serve only those who think most closely in AI style steps and language?  How will it serve those who don’t know how to talk about, or with it? AI won’t care if we don’t.

If as Loukides says, we humans are good at learning something and then applying that knowledge in a completely different area, it’s worth us thinking about how we are transferring our knowledge today to AI and how it learns from that. Not only what does AI learn in content and context, but what does it learn about learning?

His comparison of a toddler learning from parents — who in effect are ‘tagging’ objects through repetition of words while looking at images in a picture book — made me wonder how we will teach AI the benefit of learning? What incentive will it have to progress?

“the biggest project facing AI isn’t making the learning process faster and more efficient. It’s moving from machines that solve one problem very well (such as playing Go or generating imitation Rembrandts) to machines that are flexible and can solve many unrelated problems well, even problems they’ve never seen before.”

Is the skill to enable “transfer learning” what will matter most?

For AI to become truly useful, we need better as a global society to understand *where* it might best interface with our daily lives, and most importantly *why*.  And consider *who* is teaching and AI and who is being left out in the crowdsourcing of AI’s teaching.

Who is teaching AI what it needs to know?

The natural user interfaces for people to interact with today’s more common virtual assistants (Amazon’s Alexa, Apple’s Siri and Viv, Microsoft  and Cortana) are not just providing information to the user, but through its use, those systems are learning. I wonder what percentage of today’s  population is using these assistants, how representative are they, and what our AI assistants are being taught through their use? Tay was a swift lesson learned for Microsoft.

In helping shape what AI learns, what range of language it will use to develop its reference words and knowledge, society co-shapes what AI’s purpose will be —  and for AI providers to know what’s the point of selling it. So will this technology serve everyone?

Are providers counter-balancing what AI is currently learning from crowdsourcing, if the crowd is not representative of society?

So far we can only teach machines to make decisions based on what we already know, and what we can tell it to decide quickly against pre-known references using lots of data. Will your next image captcha, teach AI to separate the sloth from the pain-au-chocolat?

One of the task items for machine processing is better searches. Measurable goal driven tasks have boundaries, but who sets them? When does a computer know, if it’s found enough to make a decision. If the balance of material about the Holocaust on the web for example, were written by Holocaust deniers will AI know who is right? How will AI know what is trusted and by whose measure?

What will matter most is surely not going to be how to optimise knowledge transfer from human to AI — that is the baseline knowledge of supervised learning — and it won’t even be for AI to know when to use its skill set in one place and when to apply it elsewhere in a different context; so-called learning transfer, as Mike Loukides says. But rather, will AI reach the point where it cares?

  • Will AI ever care what it should know and where to stop or when it knows enough on any given subject?
  • How will it know or care if what it learns is true?
  • If in the best interests of advancing technology or through inaction  we do not limit its boundaries, what oversight is there of its implications?

Online limits will limit what we can reach in Thinking and Learning

If you look carefully at how humans learn online, I think rather than seeing  surprisingly little unsupervised learning, you see a lot of unsupervised questioning. It is often in the questioning that is done in private we discover, and through discovery we learn. Often valuable discoveries are made; whether in science, in maths, or important truths are found where there is a need to challenge the status quo. Imagine if Galileo had given up.

The freedom to think freely and to challenge authority, is vital to protect, and one reason why I and others are concerned about the compulsory web monitoring starting on September 5th in all schools in England, and its potential chilling effect. Some are concerned who  might have access to these monitoring results today or in future, if stored could they be opened to employers or academic institutions?

If you tell children do not use these search terms and do not be curious about *this* subject without repercussions, it is censorship. I find the idea bad enough for children, but for us as adults its scary.

As Frankie Boyle wrote last November, we need to consider what our internet history is:

“The legislation seems to view it as a list of actions, but it’s not. It’s a document that shows what we’re thinking about.”

Children think and act in ways that they may not as an adult. People also think and act differently in private and in public. It’s concerning that our private online activity will become visible to the State in the IP Bill — whether photographs that captured momentary actions in social media platforms without the possibility to erase them, or trails of transitive thinking via our web history — and third-parties may make covert judgements and conclusions about us, correctly or not, behind the scenes without transparency, oversight or recourse.

Children worry about lack of recourse and repercussions. So do I. Things done in passing, can take on a permanence they never had before and were never intended. If expert providers of the tech world such as Apple Inc, Facebook Inc, Google Inc, Microsoft Corp, Twitter Inc and Yahoo Inc are calling for change, why is the government not listening? This is more than very concerning, it will have disastrous implications for trust in the State, data use by others, self-censorship, and fear that it will lead to outright censorship of adults online too.

By narrowing our parameters what will we not discover? Not debate?  Or not invent? Happy are the clockmakers, and kids who create. Any restriction on freedom to access information, to challenge and question will restrict children’s learning or even their wanting to.  It will limit how we can improve our shared knowledge and improve our society as a result. The same is true of adults.

So in teaching AI how to learn, I wonder how the limitations that humans put on its scope — otherwise how would it learn what the developers want — combined with showing it ‘our thinking’ through search terms,  and how limitations on that if users self-censor due to surveillance, will shape what AI will help us with in future and will it be the things that could help the most people, the poorest people, or will it be people like those who programme the AI and use search terms and languages it already understands?

Who is accountable for the scope of what we allow AI to do or not? Who is accountable for what AI learns about us, from our behaviour data if it is used without our knowledge?

How far does AI have to go?

The leap for AI will be if and when AI can determine what it doesn’t know, and it sees a need to fill that gap. To do that, AI will need to discover a purpose for its own learning, indeed for its own being, and be able to do so without limitation from the that humans shaped its framework for doing so. How will AI know what it needs to know and why? How will it know, what it knows is right and sources to trust? Against what boundaries will AI decide what it should engage with in its learning, who from and why? Will it care? Why will it care? Will it find meaning in its reason for being? Why am I here?

We assume AI will know better. We need to care, if AI is going to.

How far are we away from a machine that is capable of recursive self-improvement, asks John Naughton in yesterday’s Guardian, referencing work by Yuval Harari suggesting artificial intelligence and genetic enhancements will usher in a world of inequality and powerful elites. As I was finishing this, I read his article, and found myself nodding, as I read the implications of new technology focus too much on technology and too little on society’s role in shaping it.

AI at the moment has a very broad meaning to the general public. Is it living with life-supporting humanoids?  Do we consider assistive search tools as AI? There is a fairly general understanding of “What is A.I., really?” Some wonder if we are “probably one of the last generations of Homo sapiens,” as we know it.

If the purpose of AI is to improve human lives, who defines improvement and who will that improvement serve? Is there a consensus on the direction AI should and should not take, and how far it should go? What will the global language be to speak AI?

As AI learning progresses, every time AI turns to ask its creators, “Are we there yet?”,  how will we know what to say?

image: Stephen Barling flickr.com/photos/cripsyduck (CC BY-NC 2.0)

#caredata, care.data, change, choice, confidentiality, datasharing, empowerment, ethics, HSCIC, National Pupil Database, NHS England, trust

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 One–Way Mirror: Public attitudes to commercial access to health data

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

choice, privacy, Tech, transparency, trust

Gotta know it all? Pokémon GO, privacy and behavioural research

I caught my first Pokémon and I liked it. Well, OK, someone else handed me a phone and insisted I have a go. Turns out my curve ball is pretty good. Pokémon GO is enabling all sorts of new discoveries.

Discoveries reportedly including a dead man, robbery, picking up new friends, and scrapes and bruises. While players are out hunting anime in augmented reality, enjoying the novelty, and discovering interesting fun facts about their vicinity, Pokémon GO is gathering a lot of data. It’s influencing human activity in ways that other games can only envy, taking in-game interaction to a whole new level.

And it’s popular.

But what is it learning about us as we do it?

This week questions have been asked about the depth of interaction that the app gets by accessing users’ log in credentials.

What I would like to know is what access goes in the other direction?

Google, heavily invested in AI and Machine intelligence research, has “learning systems placed at the core of interactive services in a fast changing and sometimes adversarial environment, combinations of techniques including deep learning and statistical models need to be combined with ideas from control and game theory.”

The app, which is free to download, has raised concerns over suggestions the app could access a user’s entire Google account, including email and passwords. Then it seemed it couldn’t. But Niantic is reported to have made changes to permissions to limit access to basic profile information anyway.

If Niantic gets access to data owned by Google through its use of google log in credentials, does Nantic’s investor, Google’s Alphabet, get the reverse: user data from the Google log in interaction with the app, and if so, what does Google learn through the interaction?

Who gets access to what data and why?

Brian Crecente writes that Apple, Google, Niantic likely making more on Pokémon Go than Nintendo, with 30 percent of revenue from in-app purchases on their online stores.

Next stop  is to make money from marketing deals between Niantic and the offline stores used as in-game focal points, gyms and more, according to Bryan Menegus at Gizmodo who reported Redditors had discovered decompiled code in the Android and iOS versions of Pokémon Go earlier this week “that indicated a potential sponsorship deal with global burger chain McDonald’s.”

The logical progressions of this, is that the offline store partners, i.e. McDonald’s and friends, will be making money from players, the people who get led to their shops, restaurants and cafes where players will hang out longer than the Pokéstop, because the human interaction with other humans, the battles between your collected creatures and teamwork, are at the heart of the game. Since you can’t visit gyms until you are level 5 and have chosen a team, players are building up profiles over time and getting social in real life. Location data that may build up patterns about the players.

This evening the two players that I spoke to were already real-life friends on their way home from work (that now takes at least an hour longer every evening) and they’re finding the real-life location facts quite fun, including that thing they pass on the bus every day, and umm, the Scientology centre. Well, more about that later**.

Every player I spotted looking at the phone with that finger flick action gave themselves away with shared wry smiles. All 30 something men. There is possibly something of a legacy in this they said, since the initial Pokémon game released 20 years ago is drawing players who were tweens then.

Since the app is online and open to all, children can play too. What this might mean for them in the offline world, is something the NSPCC picked up on here before the UK launch. Its focus  of concern is the physical safety of young players, citing the risk of in-game lures misuse. I am not sure how much of an increased risk this is compared with existing scenarios and if children will be increasingly unsupervised or not. It’s not a totally new concept. Players of all ages must be mindful of where they are playing**. Some stories of people getting together in the small hours of the night has generated some stories which for now are mostly fun. (Go Red Team.) Others are worried about hacking. And it raises all sorts of questions if private and public space is has become a Pokestop.

While the NSPCC includes considerations on the approach to privacy in a recent more general review of apps, it hasn’t yet mentioned the less obvious considerations of privacy and ethics in Pokémon GO. Encouraging anyone, but particularly children, out of their home or protected environments and into commercial settings with the explicit aim of targeting their spending. This is big business.

Privacy in Pokémon GO

I think we are yet to see a really transparent discussion of the broader privacy implications of the game because the combination of multiple privacy policies involved is less than transparent. They are long, they seem complete, but are they meaningful?

We can’t see how they interact.

Google has crowd sourced the collection of real time traffic data via mobile phones.  Geolocation data from google maps using GPS data, as well as network provider data seem necessary to display the street data to players. Apparently you can download and use the maps offline since Pokémon GO uses the Google Maps API. Google goes to “great lengths to make sure that imagery is useful, and reflects the world our users explore.” In building a Google virtual reality copy of the real world, how data are also collected and will be used about all of us who live in it,  is a little wooly to the public.

U.S. Senator Al Franken is apparently already asking Niantic these questions. He points out that Pokémon GO has indicated it shares de-identified and aggregate data with other third parties for a multitude of purposes but does not describe the purposes for which Pokémon GO would share or sell those data [c].

It’s widely recognised that anonymisation in many cases fails so passing only anonymised data may be reassuring but fail in reality. Stripping out what are considered individual personal identifiers in terms of data protection, can leave individuals with unique characteristics or people profiled as groups.

Opt out he feels is inadequate as a consent model for the personal and geolocational data that the app is collecting and passing to others in the U.S.

While the app provider would I’m sure argue that the UK privacy model respects the European opt in requirement, I would be surprised if many have read it. Privacy policies fail.

Poor practices must be challenged if we are to preserve the integrity of controlling the use of our data and knowledge about ourselves. Being aware of who we have ceded control of marketing to us, or influencing how we might be interacting with our environment, is at least a step towards not blindly giving up control of free choice.

The Pokémon GO permissions “for the purpose of performing services on our behalf“, “third party service providers to work with us to administer and provide the Services” and  “also use location information to improve and personalize our Services for you (or your authorized child)” are so broad as they could mean almost anything. They can also be changed without any notice period. It’s therefore pretty meaningless. But it’s the third parties’ connection, data collection in passing, that is completely hidden from players.

If we are ever to use privacy policies as meaningful tools to enable consent, then they must be transparent to show how a chain of permissions between companies connect their services.

Otherwise they are no more than get out of jail free cards for the companies that trade our data behind the scenes, if we were ever to claim for its misuse.  Data collectors must improve transparency.

Behavioural tracking and trust

Covert data collection and interaction is not conducive to user trust, whether through a failure to communicate by design or not.

By combining location data and behavioural data, measuring footfall is described as “the holy grail for retailers and landlords alike” and it is valuable.  “Pavement Opportunity” data may be sent anonymously, but if its analysis and storage provides ways to pitch to people, even if not knowing who they are individually, or to groups of people, it is discriminatory and potentially invisibly predatory. The pedestrian, or the player, Jo Public, is a commercial opportunity.

Pokémon GO has potential to connect the opportunity for profit makers with our pockets like never before. But they’re not alone.

Who else is getting our location data that we don’t sign up for sharing “in 81 towns and cities across Great Britain“?

Whether footfall outside the shops or packaged as a game that gets us inside them, public interest researchers and commercial companies alike both risk losing our trust if we feel used as pieces in a game that we didn’t knowingly sign up to. It’s creepy.

For children the ethical implications are even greater.

There are obligations to meet higher legal and ethical standards when processing children’s data and presenting them marketing. Parental consent requirements fail children for a range of reasons.

So far, the UK has said it will implement the EU GDPR. Clear and affirmative consent is needed. Parental consent will be required for the processing of personal data of children under age 16. EU Member States may lower the age requiring parental consent to 13, so what that will mean for children here in the UK is unknown.

The ethics of product placement and marketing rules to children of all ages go out the window however, when the whole game or programme is one long animated advert. On children’s television and YouTube, content producers have turned brand product placement into programmes: My Little Pony, Barbie, Playmobil and many more.

Alice Webb, Director of BBC Children’s and BBC North,  looked at some of the challenges in this as the BBC considers how to deliver content for children whilst adapting to technological advances in this LSE blog and the publication of a new policy brief about families and ‘screen time’, by Alicia Blum-Ross and Sonia Livingstone.

So is this augmented reality any different from other platforms?

Yes because you can’t play the game without accepting the use of the maps and by default some sacrifice of your privacy settings.

Yes because the ethics and implications of of putting kids not simply in front of a screen that pitches products to them, but puts them physically into the place where they can consume products – if the McDonalds story is correct and a taster of what will follow – is huge.

Boundaries between platforms and people

Blum-Ross says, “To young people, the boundaries and distinctions that have traditionally been established between genres, platforms and devices mean nothing; ditto the reasoning behind the watershed system with its roots in decisions about suitability of content. “

She’s right. And if those boundaries and distinctions mean nothing to providers, then we must have that honest conversation with urgency. With our contrived consent, walking and running and driving without coercion, we are being packaged up and delivered right to the door of for-profit firms, paying for the game with our privacy. Smart cities are exploiting street sensors to do the same.

Freewill is at the very heart of who we are. “The ability to choose between different possible courses of action. It is closely linked to the concepts of responsibility, praise, guilt, sin, and other judgments which apply only to actions that are freely chosen.” Free choice of where we shop, what we buy and who we interact with is open to influence. Influence that is not entirely transparent presents opportunity for hidden manipulation, while the NSPCC might be worried about the risk of rare physical threat, the potential for the influencing of all children’s behaviour, both positive and negative, reaches everyone.

Some stories of how behaviour is affected, are heartbreakingly positive. And I met and chatted with complete strangers who shared the joy of something new and a mutual curiosity of the game. Pokémon GOis clearly a lot of fun. It’s also unclear on much more.

I would like to explicitly understand if Pokémon GO is gift packaging behavioural research by piggybacking on the Google platforms that underpin it, and providing linked data to Google or third parties.

Fishing for frequent Pokémon encourages players to ‘check in’ and keep that behaviour tracking live. 4pm caught a Krabby in the closet at work. 6pm another Krabby. Yup, still at work. 6.32pm Pidgey on the street outside ThatGreenCoffeeShop. Monday to Friday.

The Google privacy policies changed in the last year require ten clicks for opt out, and in part, the download of an add-on. Google has our contacts, calendar events, web searches, health data, has invested in our genetics, and all the ‘Things that make you “you”. They have our history, and are collecting our present. Machine intelligence work on prediction, is the future. For now, perhaps that will be pinging you with a ‘buy one get one free’ voucher at 6.20, or LCD adverts shifting as you drive back home.

Pokémon GO doesn’t have to include what data Google collects in its privacy policy. It’s in Google’s privacy policy. And who really read that when it came out months ago, or knows what it means in combination with new apps and games we connect it with today? Tracking and linking data on geolocation, behavioural patterns, footfall, whose other phones are close by,  who we contact, and potentially even our spend from Google wallet.

Have Google and friends of Niantic gotta know it all?

comm consent, communications, confidentiality, consent, datasharing, engagement, ethics, politics, privacy, research, surveillance, Tech, transparency, trust

The illusion that might cheat us: ethical data science vision and practice

This blog post is also available as an audio file on soundcloud.

Anais Nin, wrote in her 1946 diary of the dangers she saw in the growth of technology to expand our potential for connectivity through machines, but diminish our genuine connectedness as people. She could hardly have been more contemporary for today:

“This is the illusion that might cheat us of being in touch deeply with the one breathing next to us. The dangerous time when mechanical voices, radios, telephone, take the place of human intimacies, and the concept of being in touch with millions brings a greater and greater poverty in intimacy and human vision.” [Extract from volume IV 1944-1947]

Echoes from over 70 years ago, can be heard in the more recent comments of entrepreneur Elon Musk. Both are concerned with simulation, a lack of connection between the perceived, and reality, and the jeopardy this presents for humanity. But both also have a dream. A dream based on the positive potential society has.

How will we use our potential?

Data is the connection we all have between us as humans and what machines and their masters know about us. The values that masters underpin their machine design with, will determine the effect the machines and knowledge they deliver, have on society.

In seeking ever greater personalisation, a wider dragnet of data is putting together ever more detailed pieces of information about an individual person. At the same time data science is becoming ever more impersonal in how we treat people as individuals. We risk losing sight of how we respect and treat the very people whom the work should benefit.

Nin grasped the risk that a wider reach, can mean more superficial depth. Facebook might be a model today for the large circle of friends you might gather, but how few you trust with confidences, with personal knowledge about your own personal life, and the privilege it is when someone chooses to entrust that knowledge to you. Machine data mining increasingly tries to get an understanding of depth, and may also add new layers of meaning through profiling, comparing our characteristics with others in risk stratification.
Data science, research using data, is often talked about as if it is something separate from using information from individual people. Yet it is all about exploiting those confidences.

Today as the reach has grown in what is possible for a few people in institutions to gather about most people in the public, whether in scientific research, or in surveillance of different kinds, we hear experts repeatedly talk of the risk of losing the valuable part, the knowledge, the insights that benefit us as society if we can act upon them.

We might know more, but do we know any better? To use a well known quote from her contemporary, T S Eliot, ‘Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information?’

What can humans achieve? We don’t yet know our own limits. What don’t we yet know?  We have future priorities we aren’t yet aware of.

To be able to explore the best of what Nin saw as ‘human vision’ and Musk sees in technology, the benefits we have from our connectivity; our collaboration, shared learning; need to be driven with an element of humility, accepting values that shape  boundaries of what we should do, while constantly evolving with what we could do.

The essence of this applied risk is that technology could harm you, more than it helps you. How do we avoid this and develop instead the best of what human vision makes possible? Can we also exceed our own expectations of today, to advance in moral progress?

Continue reading The illusion that might cheat us: ethical data science vision and practice →

Thinking to some purpose