Category Archives: ethics

AI, children, empowerment, engagement, ethics, human rights

Man or machine: who shapes my child? #WorldChildrensDay 2021

A reflection for World Children’s Day 2021. In ten years’ time my three children will be in their twenties. What will they and the world around them have become? What will shape them in the years in between?

Today when people talk about AI, we hear fears of consciousness in AI. We see, I, Robot.  The reality of any AI that will touch their lives in the next ten years is very different. The definition may be contested but artificial intelligence in schools already involves automated decision making at speed and scale, without compassion or conscience, but with outcomes that affect children’s lives for a long time.

The guidance of today—in policy documents, and well intentioned toolkits and guidelines and oh yes yet another ‘ethics’ framework— is all fairly same-y in terms of the issues identified.

Bias in training data. Discrimination in outcomes. Inequitable access or treatment. Lack of understandability or transparency of decision-making. Lack of routes for redress. More rarely thoughts on exclusion, disability and accessible design, and the digital divide. In seeking to fill it, the call can conclude with a cry to ensure ‘AI for all’.

Most of these issues fail to address the key questions in my mind, with regards to AI in education.

Who gets to shape a child’s life and the environment they grow up in? The special case of children is often used for special pleading in government tech issues. Despite this, in policy discussion and documents, govt. fails over and over again to address children as human beings.

Children are still developing. Physically, emotionally, their sense of fairness and justice, of humor, of politics and who they are.

AI is shaping children in ways that schools and parents cannot see.  And the issues go beyond limited agency and autonomy. Beyond the UNCRC articles 8 and 18, the role of the parent and lost boundaries between schools and home, and 23 and 29. (See at the end in detail).

Concerns about accessibility published on AI are often about the individual and inclusion, in terms of design to be able to participate. But once they can participate, where is the independent measurement and evaluation of impact on their educational progress, or physical and mental development? What is their effect?

From overhyped like Edgenuity, to the oversold like ClassCharts (that didn’t actually have any AI in it but it still won Bett Show Awards), frameworks often mention but still have no meaningful solutions for the products that don’t work and fail.

But what about the harms from products that work as intended? These can fail human dignity or create a chilling effect, like exam proctoring tech. Those safety tech that infer things and cause staff to intervene even if the child was only chatting about ‘a terraced house.’ Punitive systems that keep profiles of behaviour points long after a teacher would have let it go. What about those shaping the developing child’s emotions and state of mind by design and claim to operate within data protection law? Those who measure and track mental health or make predictions for interventions by school staff?

Brain headbands to transfer neurosignals aren’t biometric data in data protection terms if not used to or able to uniquely identify a child.

“Wellbeing” apps are not being regulated as medical devices and yet are designed to profile and influence mental health and mood and schools adopt them at scale.

If AI is being used to deliver a child’s education, but only in the English language, what risk does this tech-colonialism create in evangelising  children in non-native English speaking families through AI, not only in access to teaching, but on reshaping culture and identity?

At the institutional level, concerns are only addressed after the fact. But how should they be assessed as part of procurement when many AI are marketed as , it never stops “learning about your child”? Tech needs full life-cycle oversight, but what companies claim their products do is often only assessed to pass accreditation at a single point in time.

But the biggest gap in governance is not going to be fixed by audits or accreditation of algorithmic fairness. It is the failure to recognize the redistribution of not only agency but authority; from individuals to companies (teacher doesn’t decide what you do next, the computer does). From public interest institutions to companies (company X determines the curriculum content, not the school). And from State to companies (accountability for outcomes has fallen through the gap in outsourcing activity to the AI company). We are automating authority, and with it the shirking of responsibility, the liability for the machine’s flaws, and accepting it is the only way, thanks to our automation bias. Accountability must be human, but whose?

Around the world the rush to regulate AI, or related tech in Online Harms, or Digital Services, or Biometrics law, is going to embed, not redistribute power, through regulatory capitalism.

We have regulatory capture including on government boards and bodies that shape the agenda; unrealistic expectations of competition shaping the market; and we’re ignoring transnational colonialisation of whole schools or even regions and countries shaping the delivery of education at scale.

We’re not regulating the questions: Who does the AI serve and how do we deal with conflicts of interest between child’s rights, family, school staff, the institution or State, and the company’s wants? Where do we draw the line between public interest, private interests, and who decides what are the best interests of each child?

We’re not managing what the implications are of the datafied child being mined and analysed in order to train companies’ AI. Is it ethical or desirable to use children’s behaviour as sources of business intelligence, to donate free labour in school systems performed for companies to profit from, without any choice (see UNCRC Art 32)?

We’re barely aware as parents, if a company will decide how a child is tested in a certain way, asked certain questions about their mental health, given nudges to ‘improve’ their performance or mood.  It’s not a question of ‘is it in the best interests of a child’, but rather, who designs it and can schools assess compatibility with a child’s fundamental rights and freedoms to develop free from interference?

It’s not about protection of ‘the data’ although data protection should be about the protection of the person, not only enabling data flows for business.

It’s about protection from strangers engineering a child’s development in closed systems.

It is about child protection from unknown and unlimited number of persons interfering with who they will become.

Today’s laws and debate are too often about regulating someone else’s opinion; how it should be done, not if it should be done at all.

It is rare we read any challenge of the ‘inevitability’ of AI [in education] narrative.

Who do I ask my top two questions on AI in education:
(a) who gets and grants permission to shape my developing child, and
(b) what happens to the duty of care in loco parentis as schools outsource authority to an algorithm?

UNCRC

Article 8

1. States Parties undertake to respect the right of the child to preserve his or her identity, including nationality, name and family relations as recognised by law without unlawful interference.

Article 18

1. States Parties shall use their best efforts to ensure recognition of the principle that both parents have common responsibilities for the upbringing and development of the child. Parents or, as the case may be, legal guardians, have the primary responsibility for the upbringing and development of the child. The best interests of the child will be their basic concern.

Article 29

1. States Parties agree that the education of the child shall be directed to:

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

(c) The development of respect for the child’s parents, his or her own cultural identity, language and values, for the national values of the country in which the child is living, the country from which he or she may originate, and for civilizations different from his or her own;

Article 30

In those States in which ethnic, religious or linguistic minorities or persons of indigenous origin exist, a child belonging to such a minority or who is indigenous shall not be denied the right, in community with other members of his or her group, to enjoy his or her own culture

 

data, engagement, ethics, human rights, leadership, transparency

Ethics washing in AI. Any colour as long as it’s dark blue?

The opening discussion from the launch of the Institute for Ethics in AI in the Schwarzman Centre for Humanties in Oxford both asked many questions and left many open.

The panel event is available to watch on YouTube.

The Director recognised in his opening remarks where he expected their work to differ from the talk of ethics in AI that can become ‘matters of facile mottos hard to distinguish from corporate PR’, like “Don’t be evil.” I would like to have heard him go on to point out the reasons why, because I fear this whole enterprise is founded on just that.

My first question is whether the Institute will ever challenge its own need for existence. It is funded, therefore it is. An acceptance of the technological value and inevitability of AI is after all, built into the name of the Institute.

As Powles and Nissenbaum, wrote in 2018, “the endgame is always to “fix” A.I. systems, never to use a different system or no system at all.”

My second question is on the three drivers they went on to identify, in the same article, “Artificial intelligence… is backed by real-world forces of money, power, and data.”

So let’s follow the money.

The funder of the Schwarzman Centre for Humanties the home of the new Institute is also funding AI ethics work across the Atlantic, at Harvard, Yale and other renowned institutions that you might expect to lead in the publication of influential research. The intention at the MIT Schwarzman College of Computing, is that his investment “will reorient MIT to address the opportunities and challenges presented by the rise of artificial intelligence including critical ethical and policy considerations to ensure that the technologies are employed for the common good.” Quite where does that ‘reorientation’ seek to end up?

The panel discussed power.

The idea of ‘citizens representing citizens rather than an elite class representing citizens’, should surely itself be applied to challenge who funds work that shapes public debate. How much influence is democratic for one person to wield?

“In 2007, Mr. Schwarzman was included in TIME’s “100 Most Influential People.” In 2016, he topped Forbes Magazine’s list of the most influential people in finance and in 2018 was ranked in the Top 50 on Forbes’ list of the “World’s Most Powerful People.” [Blackstone]

The panel also talked quite a bit about data.

So I wonder what work the Institute will do in this area and the values that might steer it.

In 2020 Schwarzman’s private equity company Blackstone, acquired a majority stake in Ancestry, a provider of ‘digital family history services with 3.6 million subscribers in over 30 countries’. DNA. The Chief Financial Officer of Alphabet Inc. and Google Inc sits on Blackstone’s board. Big data. The biggest. Bloomberg reported in December 2020 that, ‘Blackstone’s Next Product May Be Data From Companies It Buys’. “Blackstone, which holds stakes in about 97 companies through its private equity funds, ramped up its data push in 2015.”

It was Nigel Shadbolt who picked up the issues of data and of representation as relates to putting human values at the centre of design. He suggested that there is growing disquiet that rather than everyday humans’ self governance, or the agency of individuals, this can mean the values of ‘organised group interests’ assert control. He picked up on the values that we most prize, as things that matter in value-based computing and later on, that transparency of data flows as a form of power being important to understand. Perhaps the striving for open data as revealing power, should also apply to funding in a more transparent, publicly accessible model?

AI in a democratic culture.

Those whose lives are most influenced by AI are often those the most excluded in discussing its harms, and rarely involved in its shaping or application. Prof Hélène Landemore (Yale University) asked perhaps the most important question in the discussion, given its wide-ranging dance around the central theme of AI and its role or effects in a democratic culture, that included Age Appropriate Design, technical security requirements, surveillance capitalism and fairness. Do we in fact have democracy or agency today at all?

It is after all not technology itself that has any intrinsic ethics but those who wield its power, those who are designing it, and shaping the future through it, those human-accountability-owners who need to uphold ethical standards in how technology controls others’ lives.

The present is already one in which human rights are infringed by machine-made and data-led decisions about us without us, without fairness, without recourse, and without redress. It is a world that includes a few individuals in control of a lot. A world in which Yassen Aslam this week said, “the conditions of work, are being hidden behind the technology.”

The ethics of influence.

I want to know what’s in it for this funder to pivot from his work life, past and present, to funding ethics in AI, and why now? He’s not renowned for his ethical approach in the world. Rather from his past at Lehman Brothers to the funding of Donald Trump, he is better known for his reported “inappropriate analogy” on Obama’s tax policies or when he reportedly compared ‘Blackstone’s unsuccessful attempt to buy a mortgage company in the midst of the subprime homeloans crisis to the devastation wreaked by an atomic bomb dropped on Hiroshima in 1945.’

In the words of the 2017 International Business Times article, How Billionaire Trump Adviser Evades Ethics Law While Shaping Policies That Make Money For His Wall Street Firm, “Schwarzman has long been a fixture in Republican politics.” “Despite Schwarzman’s formal policy role in the Trump White House, he is not technically on the White House payroll.” Craig Holman of Public Citizen, was reported as saying, “We’ve never seen this type of abuse of the ethics laws”. While politics may have moved on, we are arguably now in a time Schwarzman described as a golden age that arrives, “when you have a mess.”

The values behind the money, power, and data matter in particular because it is Oxford. Emma Briant has raised her concerns in Wired, about the report from the separate Oxford Internet Institute, Industrialized Disinformation: 2020 Global Inventory of Organized Social Media Manipulation “because of how influential the institute is.”

Will the work alone at the new ethics Institute be enough to prove that its purpose is not for the funder or his friends to use their influence to have their business interests ethics-washed in Oxford blue?  Or might what the Institute chooses not to research, say just as much? It is going to have to prove its independence and own ethical position in everything it does, and does not do, indefinitely. The panel covered a wide range of already well-discussed, popular but interesting topics in the field, so we can only wait and see.

I still think, as I did in 2019, that corporate capture is unhealthy for UK public policy. If done at scale, with added global influence, it is not only unhealthy for the future of public policy, but for academia. In this case it has the potential in practice to be at best irrelevant corporate PR, but at worst to be harmful for the direction of travel in the shaping of global attitudes towards a whole field of technology.

children, ethics, human rights, Online harms, politics, privacy

Is the Online Harms ‘Dream Ticket’ a British Green Dam?

The legal duty in Online Harms government proposals is still vague.

For some it may sound like the ‘“dream ticket”.  A framework of censorship to be decided by companies, enabled through the IWF and the government in Online Safety laws. And ‘free’ to all. What companies are already doing today in surveillance of all outgoing  and *incoming* communications that is unlawful, made lawful. Literally, the nanny state could decide, what content will be blocked, if, “such software should “absolutely” be pre-installed on all devices for children at point of sale“ and “…people could run it the other side to measure what people are doing as far as uploading content.”

From Parliamentary discussion it was clear that the government will mandate platforms, “to use automated technology…, including, where proportionate, on private channels,” even when services are encrypted.

No problem, others might say, there’s an app for that. “It doesn’t matter what program the user is typing in, or how it’s encrypted.”

But it was less clear in the consultation outcome updated yesterday,  that closed in July 2019 and still says, “we are consulting on definitions of private communications, and what measures should apply to these services.” (4.8)

Might government really be planning to impose or incentivise surveillance on [children’s] mobile phones at the point of sale in the UK? This same ‘dream ticket’ company was the only company  mentioned by the Secretary of State for DCMS yesterday. After all, it is feasible. In 2009 Chinese state media reported that the Green Dam Youth Escort service, “was only installed in 20 million computers in internet cafes and schools.“

If government thinks it would have support for such proposals, it  may have overlooked the outrage that people feel about companies prying on our everyday lives. Or has already forgotten the summer 2020 student protests over the ‘mutant algorithm’.

There is conversely already incidental harm and opaque error rates from the profiling UK children’s behaviour while monitoring their online and offline computer activity, logged against thousands of words in opaque keyword libraries. School safeguarding services are already routine in England, and are piggy backed by the Prevent programme. Don’t forget one third of referrals to Prevent come from education and over 70% are not followed through with action.  Your child and mine might already be labelled with ‘extremism’, ‘terrorism’, ‘suicide’ or ‘cyberbullying’ or have had their photos taken by the webcam of their device an unlimited number of times, thanks to some of these ‘safeguarding’ software and services, and the child and parents never know.

Other things that were not clear yesterday, but will matter, is if the ‘harm’ of the Online Harms proposals will be measured by intent, or measured by the response to it. What is harm or hate or not, is contested across different groups online, and weaponised, at scale.

The wording of the Law Commission consultation closing on Friday on communications offences also matters, and asks about intention to harm a likely audience, where harm is defined as any non-trivial emotional, psychological, or physical harm, but should not require proof of actual harm. This together with any changes on hate crime and on intimate images in effect proposes changes on ‘what’ can be said, how, and ‘to whom’ and what is considered ‘harmful’ or ‘hateful’ conduct.  It will undoubtedly have massive implications for the digital environment once all joined up. It matters when ‘culture wars’ online, can catch children in the cross fire.

I’ve been thinking about all this, against the backdrop of the Bell v Tavistock [2020] EWHC 3274 judgement with implications from the consideration of psychological harm, children’s evolving capacity, the right to be heard and their autonomy, a case where a parent involved reportedly has not even told their own child.

We each have a right to respect for our private life, our family life, our home and our correspondence. Children are rights holders in their own right. Yet it appears the government and current changes in lawmaking may soon interfere with that right in a number of ways, while children are used at the heart of everyone’s defence.

In order to find that an interference is “necessary in a democratic society” any interference with rights and freedoms should be necessary and proportionate for each individual, not some sort of ‘collective’ harm that permits a rolling, collective interference.

Will the proposed outcomes prevent children from exercising their views or full range of rights, and restrict online participation? There may be a chilling effect on speech. There is in schools. Sadly these effects may well be welcomed by those who believe not only that some rights are more equal than others, but some children, more than others.

We’ll have to wait for more details. As another MP in debate noted yesterday, “The Secretary of State rightly focused on children, but this is about more than children; it is about the very status of our society ….”

change management, empowerment, ethics

The devil craves DARPA

‘People, ideas, machines — in that order.’ This quote in that  latest blog by Dominic Cummings is spot on, but the blind spots or the deliberate scoping the blog reveals, are both just as interesting.

If you want to “figure out what characters around Putin might do”, move over Miranda. If your soul is for sale, then this might be the job for you. This isn’t anthropomorphism of Cummings, but an excuse to get in the parallels to Meryl Streep’s portrayal of Priestly.

“It will be exhausting but interesting and if you cut it you will be involved in things at the age of 21 that most people never see.”

Comments like these make people who are not of that mould, feel of less worth. Commitment comes in many forms. People with kids and caring responsibilities, may be some of your most loyal staff. You may not want them as your new PA, but you will almost certainly, not want to lose them across the board.

Some words would be wise in follow up to existing staff, the thousands of public servants we have today, after his latest post.

1. The blog is aimed at a certain kind of men. Speak to women too.

The framing of this call for staff is problematic, less for its suggested work ethic, than the structural inequalities it appears to purposely perpetuate. Despite the poke at public school bluffers. Do you want the best people around you, able to play well with others, or not?

I am disappointed that asking for “the sort of people we need to find” is designed, intentionally or not, to appeal to a certain kind of men. Even if he says it should be diverse and includes people, “like that girl hired by Bigend as a brand ‘diviner.'”

If Cummings is intentional about hiring the best people, then he needs to do by better by women. We already have a PM that many women would consider toxic to work around, and won’t as a result.

Some of the most brilliant, cognitively diverse, young people I know who fit these categories well, — and across the political spectrum–are themselves diverse by nature and expect their surroundings to be. They (unlike our generation), do not “babble about ‘gender identity diversity blah blah’.” Woke is not an adjective that needs explained, but a way of life. Put such people off by appearing to devalue their norms, and you’ll miss out on some potential brilliant applicants from the pool, which will already be self-selecting, excluding many who simply won’t work for you, or Boris, or Brexit blah blah. People prepared to burn out as you want them to, aren’t going to be at their best for long. And it takes a long time to recover.

‘That girl’ was the main character, and her name was Cayce Pollard.  Women know why you should say her name. Fewer women will have worked at CERN, perhaps for related reasons, compared with “the ideal candidate” described in this call.

“If you want an example of the sort of people we need to find in Britain, look at this’ he writes of C.C. Myers, with a link to, ‘On the Cover:  The World’s Fastest Man.“

Charlie Munger, Warren Buffett, Alexander Grothendieck, Bret Victor, von Neumann, Cialdini. Groves, Mueller, Jain, Pearl, Kay, Gibson, Grove, Makridakis, Yudkowsky, Graham and Thiel.

The *men illustrated* list, goes on and on.

What does it matter how many lovers you have if none of them gives you the universe?

Not something I care to discuss over dinner either.

But women of all ages do care that our PM appears to be a cad. It matters therefore that your people be seen to work to a better standard. You want people loyal to your cause, and the public to approve, even if they don’t of your leader. Leadership goes far beyond electoral numbers and a mandate.

Women — including those that tick the skill boxes need, yet again, to look beyond the numbers and have to put up with a lot. This advertorial appeals to Peter Parker, when the future needs more of Miles Morales. Fewer people with the privilege and opportunity to work at the Large Hadron Collider, and more of those who stop Kingpin’s misuse and shut it down.

A different kind of the same kind of thing, isn’t real change. This call for something new, is far less radical than it is being portrayed as.

2. Change. Don’t forget to manage it by design.

In fact, the speculation that this is all change, hiring new people for new stuff [some of which elsewhere he has genuinely interesting ideas on, like, “decentralisation and distributed control to minimise the inevitable failures of even the best people”] doesn’t really feature here, rather it is something of a precursor. He’s starting less with building the new, and rather with let’s ‘drain the swamp’ of bureaucracy. The Washington-style of 1980’s Reagan, including, ‘let’s put in some more of our kind of people’.

His personal brand of longer-term change may not be what some of his cheerleaders think it will be, but if the outcome is the same and seen to be ‘showing these Swamp creatures the zero mercy they deserve‘, [sic] does intent matter? It does, and he needs to describe his future plans better, if he wants to have a civil service  that works well.

The biggest content gap (leaving actual policy content aside) is any appreciation of the current, and need for change management.

Training gets a mention; but new process success, depends on effectively communicating on change, and delivering training about it to all, not only those from whom you expect the most high performance. People not projects, remember?

Change management and capability transfer delivered by costly consultants, is not needed, but making it understandable not elitist, is.

  • genuinely present an understanding of the as-is,  (I get you and your org, for change *with* you, not to force change upon you)
  • communicating what the future model is going to move towards (this is why you want to change and what good looks like), and
  • a roadmap of how you expect the organisation to get there (how and when), that need not be constricted by artificial comms grids.

Because people and having their trust, are what make change work.

On top of the organisational model, *every* member of staff must know where their own path fits in, and if their role is under threat, whether training will be offered to adapt, or whether they will be made redundant. Uncertainty around this over time, is also toxic. You might not care if you lose people along the way. You might consider these the most expendable people. But if people are fearful and unhappy in your organisation, or about their own future, it will hold them back from delivering at their best, and the organisation as a result.  And your best will leave, as much as those who are not.

“How to build great teams and so on”, is not a bolt-on extra here, it is fundamental.  You can’t forget the kitchens. But changing the infrastructure alone, cannot deliver real change you want to see.

3. Communications. Neither propaganda and persuasion nor PR.

There is not such a vast difference between the business of communications as a campaign tool, and tool for control. Persuasion and propaganda. But where there may be a blind spot in the promotion of the Cialdini-six style comms, is that behavioural scientists that excel at these, will not use the kind of communication tools that either the civil service nor the country needs for the serious communications of change, beyond the immediate short term.

Five thoughts:

  1. Your comms strategy should simply be “Show the thing. Be clear. Be brief.”
  2. Communicating that failure is acceptable, is only so if it means learning from it.
  3. If policy comms plans depend on work led by people like you,  who like each other and like you, you’ll be told what you want to hear.
  4. Ditto, think tanks that think the same are not as helpful as others.
  5. And you need grit in the oyster for real change.

As an aside, for anyone having kittens about using an unofficial email to get around FOI requests and think it a conspiracy to hide internal communications, it really doesn’t work that way. Don’t panic, we know where our towel is.

4. The Devil craves DARPA. Build it with safe infrastructures.

Cumming’s long-established fetishing of technology and fascination with Moscow will be familiar to those close, or blog readers. They are also currently fashionable, again. The solution is therefore no surprise, and has been prepped in various blogs for ages. The language is familiar. But single-mindedness over this length of time, can make for short sightedness.

In the US. DARPA was set up in 1958 after the Soviet Union launched the world’s first satellite, with a remit to “prevent technological surprise” and pump money into “high risk, high reward” projects. (Sunday Times, Dec 28, 2019)

In  March, Cummings wrote in praise of Project Maven;

“The limiting factor for the Pentagon in deploying advanced technology to conflict in a useful time period was not new technical ideas — overcoming its own bureaucracy was harder than overcoming enemy action.”

Almost a year after that project collapsed, its most interesting feature was surely not the role of bureaucracy among tech failure. Maven was a failure not of tech, nor bureaucracy, but to align its values with the decency of its workforce. Whether the recallibration of its compass as a company is even possible, remains to be seen.

If firing staff who hold you to account against a mantra of ‘don’t be evil’ is championed, this drive for big tech values underpinning your staff thinking and action, will be less about supporting technology moonshots, than a shift to the Dark Side of capitalist surveillance.

The incessant narrative focus on man and the machine –machine learning, ⁠—the machinery of government, quantitative models and the frontiers of the science of prediction is an obsession with power. The downplay of the human in that world ⁠—is displayed in so many ways, but the most obvious is the press and political narrative of a need to devalue human rights, ⁠— and yet to succeed, tech and innovation needs an equal and equivalent counterweight, in accountability under human rights and the law, so that when systems fail people, they do not cause catastrophic harm at scale.

“Practically nobody is ever held accountable regardless of the scale of failure,“ you say? How do you measure your own failure? Or the failure of policy? Transparency over that, and a return to Ministerial accountability are changes I would like to see. Or how about demanding accountability for algorithms that send children to social care, of which the CEO has said his failure is only measured by a Local Authority not saving money as a result of using their system?

We must stop state systems failing children, if they are not to create a failed society.

A UK DARPA-esque, devolved hothousing for technology will fail, if you don’t shore up public trust. Both in the state and commercial sectors. An electoral mandate won’t last, nor reach beyond its scope for long. You need a social licence to have legitimacy for tech that uses public data, that is missing today. It is bone-headed and idiotic that we can’t get this right as a country.  Despite knowing how to, if government keeps avoiding doing it safely, it will come at a cost.

The Pentagon certainly cares about the implications for national security when the personal data of millions of people could be open to exploitation, blackmail or abuse.

You might of course, not care. But commercial companies will when they go under. The electorate will. Your masters might if their legacy will suffer and debate about the national good and the UK as a Life Sciences centre, all come to naught.

There was little in this blog, of the reality of what these hires should deliver beyond more tech and systems’ change. But the point is to make systems that work for people, not see more systems at work.

We could have it all, but not if you spaff our data laws up the wall.

“But the ship can’t sink.”

“She is made of iron, sir. I assure you, she can. And she will. It is a mathematical certainty.“

[Attributed to Thomas Andrews, Chief Designer of the RMS Titanic.]

5. The ‘circle of competence’ needs values, not only to value skills.

It’s important and consistent behaviour that Cummings says he recognises his own weaknesses, that some decisions are beyond his ‘circle of competence’ and that he should in in effect become redundant, having brought in, “the sort of expertise supporting the PM and ministers that is needed.” Founder’s syndrome is common to organisations and politics is not exempt. But neither is the Peter principle a phenomenon particular to only the civil service.

“One of the problems with the civil service is the way in which people are shuffled such that they either do not acquire expertise or they are moved out of areas they really know to do something else.”

But so what? what’s worse, is politics has not only the Peter’s but the Dilbert principle when it comes to senior leadership. You can’t put people in positions expected to command respect when they tell others to shut up and go away. Or fire without due process. If you want orgs to function together at scale, especially beyond the current problems with silos, they need people on the ground who can work together, and have a common goal who respect those above them, and feel it is all worthwhile. Their politics don’t matter. But integrity, respect and trust do, even if they don’t matter to you personally.

I agree wholeheartedly that circles of competence matter [as I see the need to build some in education on data and edTech]. Without the appropriate infrastructure change, radical change of policy is nearly impossible. But skill is not the only competency that counts when it comes to people.

If the change you want is misaligned with people’s values, people won’t support it, no matter who you get to see it through. Something on the integrity that underpins this endeavour,  will matter to the applicants too. Most people do care how managers treat their own.

The blog was pretty clear that Cummings won’t value staff, unless their work ethic, skills and acceptance will belong to him alone to judge sufficient or not, to be “binned within weeks if you don’t fit.”

This government already knows it has treated parts of the public like that for too long. Policy has knowingly left some people behind on society’s  scrap heap, often those scored by automated systems as inadequate. Families in-work moved onto Universal Credit, feed their children from food banks for #5WeeksTooLong. The rape clause. Troubled families. Children with special educational needs battling for EHC plan recognition without which schools won’t take them, and DfE knowingly underfunding suitable Alternative Provision in education by a colossal several hundred per cent amount per place, by design.

The ‘circle of competence’ needs to recognise what happens as a result of policy, not only to place value on the skills in its delivery or see outcomes on people as inevitable or based on merit. Charlie Munger may have said, “At the end of the day – if you live long enough – most people get what they deserve.”

An awful lot of people deserve a better standard of living and human dignity than the UK affords them today. And we can’t afford not to fix it. A question for new hires: How will you contribute to doing this?

6. Remember that our civil servants, are after all, public servants.  

The real test of competence, and whether the civil service delivers for the people whom they serve, is inextricably bound with government policy. If its values, if its ethics are misguided, building a new path with or without new people, will be impossible.

The best civil servants I have worked with, have one thing in common. They have a genuine desire to make the world better. [We can disagree on what that looks like and for whom, on fraud detection, on immigration, on education, on exploitation of data mining and human rights, or the implications of the law. Their policy may bring harm, but their motivation is not malicious.] Your goal may be a ‘better’ civil service. They may be more focussed on better outcomes for people, not systems. Lose sight of that, and you put the service underpinning government, at risk. Not to bring change for good, but to destroy the very point of it.  Keep the point of a better service, focussed on the improvement for the public.

Civil servants civilly serve in the words of Stefan Czerniawski. These plans will need challenge to be the best they can be. As pubstrat asked, so should we all ask Cummings to outline his thoughts on:

  • “What makes the decisions which civil servants implement legitimate?
  • Where are the boundaries of that legitimacy and how can they be detected?
  • What should civil servants do if those boundaries are reached and crossed?”

Self-destruction for its own sake, is not a compelling narrative for change, whether you say you want to control that narrative, or not.

Two hands are a lot, but many more already work in the civil service. If Cummings only works against them, he’ll succeed not in building change, but resistance.

deregulation, empowerment, ethics, GDPR, human rights

When FAT fails: Why we need better process infrastructure in public services.

I’ve been thinking about FAT, and the explainability of decision making.

There may be few decisions about people at scale, today in the public sector, in which computer stored data aren’t used. For some, computers are used to make or help make decisions.

How we understand those decisions in a vital part of the obligation of fairness, in data processing. How I know that *you* have data about me, and are processing it, in order to make a decision that affects me. So there’s an awful lot of good things that come out of that. The staff member does their job with better understanding. The person affected has an opportunity to question and correct if necessary, the inputs to the decision. And one hopes, that the computer support can make many decisions faster, and with more information in useful ways, than the human staff member alone.

But, why then, does it seem so hard to get this understood and processes in place to make the decision making understandable?

And more importantly, why does there seem to be no consistency in how such decision-making is documented, and communicated?

From school progress measures, to PIP and Universal Credit applications, to predictive  ‘risk scores’ for identifying gang membership and child abuse. In a world where you need to be computer literate but there may be no computer to help you make an application, the computers behind the scenes are making millions of life changing decisions.

We cannot see them happen, and often don’t see the data that goes into them. From start to finish, it is a hidden process.

The current focus on FAT —  fairness, accountability, and transparency of algorithmic systems — often makes accountability for the computer part of the decision-making in the public sector, appear something that has become too hard to solve and needs complex thinking around.

I want conversations to go back to something more simple. Humans taking responsibility for their actions. And to do so, we need better infrastructure for whole process delivery, where it involves decision making, in public services.

Academics, boards, conferences, are all spending time on how to make the impact of the algorithms fair, accountable, and transparent. But in the search for ways to explain legal and ethical models of fairness, and to explain the mathematics and logic behind algorithmic systems and machine learning, we’ve lost sight of why anyone needs to know. Who cares and why?

People need to get redress when things go wrong or appear to be wrong. If things work, the public at large generally need not know why.  Take TOEIC. “The way the Home Office has treated these students makes a mockery of the British justice system. And the impact has been devastating.“ Yet there is no mechanism for redress and no one in government has taken responsibility for its failures.

That’s a policy decision taken by people.

Routes for redress on decisions today are often about failed policy and processes. They are costly and inaccessible, such as fighting Local Authorities decisions not to provide services required by law.

That’s a policy decision taken by people.

Rather in the same way that the concept of ethics has become captured and distorted by companies to suit their own agenda, so if anything, the focus on FAT has undermined the concept of whole process audit and responsibility for human choices, decisions, and actions.

The effect of a machine-made decision on those who are included in the system response, — and more rarely those who may be left out of it, or its community effects, — has been singled out for a lot of people’s funding and attention as what matters to understand and audit in the use of data for making safe and just decisions.

It’s right to do so, but not as a stand alone cog in the machine.

The computer and its data processing have been unjustifiably deified. Rather than supporting public sector staff they are disempowered in the process as a whole. It is assumed the computer knows best, and can be used to justify a poor decision — “well, what could I do, the data told me to do it?” is rather like, “it was not my job to pick up the fax from the fax machine.” But that’s not a position we should encourage.

We have become far too accommodating of this automated helplessness.

If society feels a need to take back control, as a country and of our own lives, we also need to see decision makers take back responsibility.

The focus on FAT emphasises the legal and ethical obligations on companies and organisations, to be accountable for what the computer says, and the narrow algorithmic decision(s) in it.  But it is rare that an outcome in most things in real life, is the result of a singular decision.

So does FAT fit these systems at all?

Do I qualify for PIP? Can your child meet the criteria needed for additional help at school?  Does the system tag your child as part of a ‘Troubled Family’? These outcomes are life affecting in the public sector. It should therefore be made possible to audit *if* and *how* the public sector should offer to change lives as a holistic process.

That means re-looking at if and how we audit that whole end-to-end process > from policy idea, to legislation, through design to delivery.

There are no simple, clean, machine readable results in that.

Yet here again, the current system-process-solution encourages the public sector to use *data* to assess and incentivise the process to measure the process, and award success and failure, packaged into surveys and payment-by-results.

The data driven measurement, assesses data driven processes, that compound the problems of this infinite human-out-of-the-loop.

This clean laser-like focus misses out on the messy complexity of our human lives.  And the complexity of public service provision makes it very hard to understand the process of delivery. As long as the end-to-end system remains weighted to self preservation, to minimise financial risk to the institution for example, or to find a targeted number of interventions, people will be treated unfairly.

Through a hyper focus on algorithms and computer-led decision accountability, the tech sector, academics and everyone involved, is complicit in a debate that should be about human failure. We already have algorithms in every decision process. Human and machine-led algorithms. Before we decide if we need a new process of fairness, accountability and transparency, we should know who’s responsible now for the outcomes and failure in any given activity, and ask, ‘Does it really need to change?’

To restore some of the power imbalance to the public on decisions about us made by authorities today, we urgently need public bodies to compile, publish and maintain at very minimum, some of the basic underpinning and auditable infrastructure — the ‘plumbing’ — inside these processes:

  1. a register of data analytics systems used by Local and Central Government, including but not only those where algorithmic decision-making affects individuals.
  2. a register of data sources used in those analytics systems.
  3. a consistently identifiable and searchable taxonomy of the companies and third-parties delivering those analytics systems.
  4. a diagrammatic mapping of core public service delivery activities, to understand the tasks, roles, and responsibilities within the process. It would benefit government at all levels to be able to see themselves where decision points sit, understand flows of data and cash, and see where which law supports the task, and accountability sits.

Why? Because without knowing what is being used at scale, how and by whom, we are poorly informed and stay helpless. It allows for enormous and often unseen risks without adequate checks and balances — like named records with the sexual orientation data of almost 3.2 million people, and religious belief data on 3.7 million sitting in multiple distributed databases — and with the massive potential for state-wide abuse by any current or future government.  And the responsibility for each part of a process remains unclear.

If people don’t know what you’re doing, they don’t know what you’re doing wrong, after all. But it also means the system is weighted unfairly against people. Especially those who least fit the model.

We need to make increasingly lean systems more fat and stuff them with people power again. Yes we need fairness accountability and transparency. But we need those human qualities to reach across thinking beyond computer code. We need to restore humanity to automated systems and it has to be re-instated across whole processes.

FAT focussed only on computer decisions, is a distraction from auditing failure to deliver systems that work for people. It’s a failure to manage change and of governance, and to be accountable for when things go wrong.

What happens when FAT fails? Who cares and what do they do?

AI, change management, datasharing, deregulation, ethics, GDPR, politics, privacy, trust

Policy shapers, product makers, and profit takers (1)

In 2018, ethics became the new fashion in UK data circles.

The launch of the Women Leading in AI principles of responsible AI, has prompted me to try and finish and post these thoughts, which have been on my mind for some time. If two parts of 1K is tl:dr for you, then in summary, we need more action on:

  • Ethics as a route to regulatory avoidance.
  • Framing AI and data debates as a cost to the Economy.
  • Reframing the debate around imbalance of risk.
  • Challenging the unaccountable and the ‘inevitable’.

And in the next post on:

  • Corporate Capture.
  • Corporate Accountability, and
  • Creating Authentic Accountability.

Ethics as a route to regulatory avoidance

In 2019, the calls to push aside old wisdoms for new, for everyone to focus on the value-laden words of ‘innovation’ and ‘ethics’, appears an ever louder attempt to reframe regulation and law as barriers to business, asking to cast them aside.

On Wednesday evening, at the launch of the Women Leading in AI principles of responsible AI, the chair of the CDEI said in closing, he was keen to hear from companies where, “they were attempting to use AI effectively and encountering difficulties due to regulatory structures.”

In IBM’s own words to government recently,

“A rush to further regulation can have the effect of chilling innovation and missing out on the societal and economic benefits that AI can bring.”

The vague threat is very clear, if you regulate, you’ll lose. But the the societal and economic benefits are just as vague.

So far, many talking about ethics are trying to find a route to regulatory avoidance. ‘We’ll do better,’ they promise.

In Ben Wagner’s recent paper, “Ethics as an Escape from Regulation: From ethics-washing to ethics-shopping,” he asks how to ensure this does not become the default engagement with ethical frameworks or rights-based design. He sums up, “In this world, ‘ethics’ is the new ‘industry self-regulation.”

Perhaps it’s ingenious PR to make sure that what is in effect self-regulation, right across the business model, looks like it comes imposed from others, from the very bodies set up to fix it.

But as I think about in part 2, is this healthy for UK public policy and the future not of an industry sector, but a whole technology, when it comes to AI?

Framing AI and data debates as a cost to the Economy

Companies, organisations and individuals arguing against regulation are framing the debate as if it would come at a great cost to society and the economy. But we rarely hear, what effect do they expect on their company. What’s the cost/benefit expected for them. It’s disingenuous to have only part of that conversation. In fact the AI debate would be richer were it to be included. If companies think their innovation or profits are at risk from non-use, or regulated use, and there is risk to the national good associated with these products, we should be talking about all of that.

And in addition, we can talk about use and non-use in society. Too often, the whole debate is intangible. Show me real costs, real benefits. Real risk assessments. Real explanations that speak human. Industry should show society what’s in it for them.

You don’t want it to ‘turn out like GM crops’? Then learn their lessons on transparency, trustworthiness, and avoid the hype. And understand sometimes there is simply tech, people do not want.

Reframing the debate around imbalance of risk

And while we often hear about the imbalance of power associated with using AI, we also need to talk about the imbalance of risk.

While a small false positive rate for a company product may be a great success for them, or for a Local Authority buying the service, it might at the same time, mean lives forever changed, children removed from families, and individual reputations ruined.

And where company owners may see no risk from the product they assure is safe, there are intangible risks that need factored in, for example in education where a child’s learning pathway is determined by patterns of behaviour, and how tools shape individualised learning, as well as the model of education.

Companies may change business model, ownership, and move on to other sectors after failure. But with the levels of unfairness already felt in the relationship between the citizen and State — in programmes like Troubled Families, Universal Credit, Policing, and Prevent — where use of algorithms and ever larger datasets is increasing, long term harm from unaccountable failure will grow.

Society needs a rebalance of the system urgently to promote transparent fairness in interactions, including but not only those with new applications of technology.

We must find ways to reframe how this imbalance of risk is assessed, and is distributed between companies and the individual, or between companies and state and society, and enable access to meaningful redress when risks turn into harm.

If we are to do that, we need first to separate truth from hype, public good from self-interest and have a real discussion of risk across the full range from individual, to state, to society at large.

That’s not easy against a non-neutral backdrop and scant sources of unbiased evidence and corporate capture.

Challenging the unaccountable and the ‘inevitable’.

In 2017 the Care Quality Commission reported into online services in the NHS, and found serious concerns of unsafe and ineffective care. They have a cross-regulatory working group.

By contrast, no one appears to oversee that risk and the embedded use of automated tools involved in decision-making or decision support, in children’s services, or education. Areas where AI and cognitive behavioural science and neuroscience are already in use, without ethical approval, without parental knowledge or any transparency.

Meanwhile, as all this goes on, academics many are busy debating fixing algorithmic bias, accountability and its transparency.

Few are challenging the narrative of the ‘inevitability’ of AI.

Julia Powles and Helen Nissenbaum recently wrote that many of these current debates are an academic distraction, removed from reality. It is under appreciated how deeply these tools are already embedded in UK public policy. “Trying to “fix” A.I. distracts from the more urgent questions about the technology. It also denies us the possibility of asking: Should we be building these systems at all?”

Challenging the unaccountable and the ‘inevitable’ is the title of the conclusion of the Women Leading in AI report on principles, and makes me hopeful.

“There is nothing inevitable about how we choose to use this disruptive technology. […] And there is no excuse for failing to set clear rules so that it remains accountable, fosters our civic values and allows humanity to be stronger and better.”

[1] Powles, Nissenbaum, 2018,The Seductive Diversion of ‘Solving’ Bias in Artificial Intelligence, Medium

Next: Part  2– Policy shapers, product makers, and profit takers on

  • Corporate Capture.
  • Corporate Accountability, and
  • Creating Authentic Accountability.
commercial, datasharing, deregulation, ethics, privacy, Tech, transparency, trust

Policy shapers, product makers, and profit takers (2)

Corporate capture

Companies are increasingly in controlling positions of the tech narrative in the press. They are funding neutral third-sector orgs’ and think tanks’ research. Supporting organisations advising on online education. Closely involved in politics. And sit increasingly, within the organisations set up to lead the technology vision, advising government on policy and UK data analytics, or on social media, AI and ethics.

It is all subject to corporate capture.

But is this healthy for UK public policy and the future not of an industry sector, but a whole technology, when it comes to AI?

If a company’s vital business interests seem unfazed by the risk and harm they cause to individuals — from people who no longer trust the confidentiality of the system to measurable harms — why should those companies sit on public policy boards set up to shape the ethics they claim we need, to solve the problems and restore loss of trust that these very same companies are causing?

We laud people in these companies as co-founders and forward thinkers on new data ethics institutes. They are invited to sit on our national boards, or create new ones.

What does that say about the entire board’s respect for the law which the company breached? It is hard not to see it signal acceptance of the company’s excuses or lack of accountability.

Corporate accountability

The same companies whose work has breached data protection law, multiple ways, seemingly ‘by accident’ on national data extractions, are those companies that cross the t’s and dot the i’s on even the simplest conference call, and demand everything is said in strictest confidence. Meanwhile their everyday business practices ignore millions of people’s lawful rights to confidentiality.

The extent of commercial companies’ influence on these boards is  opaque. To allow this ethics bandwagon to be driven by the corporate giants surely eschews genuine rights-based values, and long-term integrity of the body they appear to serve.

I am told that these global orgs must be in the room and at the table, to use the opportunity to make the world a better place.

These companies already have *all* the opportunity. Not only monopoly positions on their own technology, but the datasets at scale which underpin it, excluding new entrants to the market. Their pick of new hires from universities. The sponsorship of events. The political lobbying. Access to the media. The lawyers. Bottomless pockets to pay for it all. And seats at board tables set up to shape UK policy responses.

It’s a struggle for power, and a stake in our collective future. The status quo is not good enough for many parts of society, and to enable Big Tech or big government to maintain that simply through the latest tools, is a missed chance to reshape for good.

You can see it in many tech boards’ make up, and pervasive white male bias. We hear it echoed in London think tank conferences, even independent tech design agencies, or set out in some Big Tech reports. All seemingly unconnected, but often funded by the same driving sources.

These companies are often those that made it worse to start with, and the very ethics issues the boards have been set up to deal with, are at the core of their business models and of their making.

The deliberate infiltration of influence on online safety policy for children, or global privacy efforts is very real, explicitly set out in the #FacebookEmails, for example.

We will not resolve these fundamental questions, as long as the companies whose business depend on them, steer national policy. The odds will be ever in their favour.

At the same time, some of these individuals are brilliant. In all senses.

So what’s the answer. If they are around the table, what should the UK public expect of their involvement, and ensure in whose best interests it is? How do we achieve authentic accountability?

Whether it be social media, data analytics, or AI in public policy, can companies be safely permitted to be policy shapers if they wear all the hats; product maker, profit taker, *and* process or product auditor?

Creating Authentic Accountability

At minimum we must demand responsibility for their own actions from board members who represent or are funded by companies.

  1. They must deliver on their own product problems first before being allowed to suggest solutions to societal problems.
  2. There should be credible separation between informing policy makers, and shaping policy.
  3. There must be total transparency of funding sources across any public sector boards, of members, and those lobbying them.
  4. Board members must be meaningfully held accountable for continued company transgressions on rights and freedoms, not only harms.
  5. Oversight of board decision making must be decentralised, transparent and available to scrutiny and meaningful challenge.

While these new bodies may propose solutions that include public engagement strategies, transparency, and standards, few propose meaningful oversight. The real test is not what companies say in their ethical frameworks, but in what they continue to do.

If they fail to meet legal or regulatory frameworks, minimum accountability should mean no more access to public data sets and losing positions of policy influence.

Their behaviour needs to go above and beyond meeting the letter of the law, scraping by or working around rights based protections. They need to put people ahead of profit and self interests. That’s what ethics should mean, not be a PR route to avoid regulation.

As long as companies think the consequences of their platforms and actions are tolerable and a minimal disruption to their business model, society will be expected to live with their transgressions, and our most vulnerable will continue to pay the cost.

This is part 2 of thoughts on Policy shapers, product makers, and profit takers — data and AI. Part 1 is here.

AI, change management, children, datasharing, deregulation, engagement, ethics, politics, privacy, pupils, research, surveillance, Tech, transparency, trust

The power of imagination in public policy

“A new, a vast, and a powerful language is developed for the future use of analysis, in which to wield its truths so that these may become of more speedy and accurate practical application for the purposes of mankind than the means hitherto in our possession have rendered possible.” [on Ada Lovelace, The First tech Visionary, New Yorker, 2013]

What would Ada Lovelace have argued for in today’s AI debates? I think she may have used her voice not only to call for the good use of data analysis, but for her second strength.The power of her imagination.

James Ball recently wrote in The European [1]:

“It is becoming increasingly clear that the modern political war isn’t one against poverty, or against crime, or drugs, or even the tech giants – our modern political era is dominated by a war against reality.”

My overriding take away from three days spent at the Conservative Party Conference this week, was similar. It reaffirmed the title of a school debate I lost at age 15, ‘We only believe what we want to believe.’

James writes that it is, “easy to deny something that’s a few years in the future“, and that Conservatives, “especially pro-Brexit Conservatives – are sticking to that tried-and-tested formula: denying the facts, telling a story of the world as you’d like it to be, and waiting for the votes and applause to roll in.”

These positions are not confined to one party’s politics, or speeches of future hopes, but define perception of current reality.

I spent a lot of time listening to MPs. To Ministers, to Councillors, and to party members. At fringe events, in coffee queues, on the exhibition floor. I had conversations pressed against corridor walls as small press-illuminated swarms of people passed by with Queen Johnson or Rees-Mogg at their centre.

In one panel I heard a primary school teacher deny that child poverty really exists, or affects learning in the classroom.

In another, in passing, a digital Minister suggested that Pupil Referral Units (PRU) are where most of society’s ills start, but as a Birmingham head wrote this week, “They’ll blame the housing crisis on PRUs soon!” and “for the record, there aren’t gang recruiters outside our gates.”

This is no tirade on failings of public policymakers however. While it is easy to suspect malicious intent when you are at, or feel, the sharp end of policies which do harm, success is subjective.

It is clear that an overwhelming sense of self-belief exists in those responsible, in the intent of any given policy to do good.

Where policies include technology, this is underpinned by a self re-affirming belief in its power. Power waiting to be harnessed by government and the public sector. Even more appealing where it is sold as a cost-saving tool in cash strapped councils. Many that have cut away human staff are now trying to use machine power to make decisions. Some of the unintended consequences of taking humans out of the process, are catastrophic for human rights.

Sweeping human assumptions behind such thinking on social issues and their causes, are becoming hard coded into algorithmic solutions that involve identifying young people who are in danger of becoming involved in crime using “risk factors” such as truancy, school exclusion, domestic violence and gang membership.

The disconnect between perception of risk, the reality of risk, and real harm, whether perceived or felt from these applied policies in real-life, is not so much, ‘easy to deny something that’s a few years in the future‘ as Ball writes, but a denial of the reality now.

Concerningly, there is lack of imagination of what real harms look like.There is no discussion where sometimes these predictive policies have no positive, or even a negative effect, and make things worse.

I’m deeply concerned that there is an unwillingness to recognise any failures in current data processing in the public sector, particularly at scale, and where it regards the well-known poor quality of administrative data. Or to be accountable for its failures.

Harms, existing harms to individuals, are perceived as outliers. Any broad sweep of harms across policy like Universal Credit, seem perceived as political criticism, which makes the measurable failures less meaningful, less real, and less necessary to change.

There is a worrying growing trend of finger-pointing exclusively at others’ tech failures instead. In particular, social media companies.

Imagination and mistaken ideas are reinforced where the idea is plausible, and shared. An oft heard and self-affirming belief was repeated in many fora between policymakers, media, NGOs regards children’s online safety. “There is no regulation online”. In fact, much that applies offline applies online. The Crown Prosecution Service Social Media Guidelines is a good place to start. [2] But no one discusses where children’s lives may be put at risk or less safe, through the use of state information about them.

Policymakers want data to give us certainty. But many uses of big data, and new tools appear to do little more than quantify moral fears, and yet still guide real-life interventions in real-lives.

Child abuse prediction, and school exclusion interventions should not be test-beds for technology the public cannot scrutinise or understand.

In one trial attempting to predict exclusion, this recent UK research project in 2013-16 linked children’s school records of 800 children in 40 London schools, with Metropolitan Police arrest records of all the participants. It found interventions created no benefit, and may have caused harm. [3]

“Anecdotal evidence from the EiE-L core workers indicated that in some instances schools informed students that they were enrolled on the intervention because they were the “worst kids”.”

“Keeping students in education, by providing them with an inclusive school environment, which would facilitate school bonds in the context of supportive student–teacher relationships, should be seen as a key goal for educators and policy makers in this area,” researchers suggested.

But policy makers seem intent to use systems that tick boxes, and create triggers to single people out, with quantifiable impact.

Some of these systems are known to be poor, or harmful.

When it comes to predicting and preventing child abuse, there is concern with the harms in US programmes ahead of us, such as both Pittsburgh, and Chicago that has scrapped its programme.

The Illinois Department of Children and Family Services ended a high-profile program that used computer data mining to identify children at risk for serious injury or death after the agency’s top official called the technology unreliable, and children still died.

“We are not doing the predictive analytics because it didn’t seem to be predicting much,” DCFS Director Beverly “B.J.” Walker told the Tribune.

Many professionals in the UK share these concerns. How long will they be ignored and children be guinea pigs without transparent error rates, or recognition of the potential harmful effects?

Helen Margetts, Director of the Oxford Internet Institute and Programme Director for Public Policy at the Alan Turing Institute, suggested at the IGF event this week, that stopping the use of these AI in the public sector is impossible. We could not decide that, “we’re not doing this until we’ve decided how it’s going to be.” It can’t work like that.” [45:30]

Why on earth not? At least for these high risk projects.

How long should children be the test subjects of machine learning tools at scale, without transparent error rates, audit, or scrutiny of their systems and understanding of unintended consequences?

Is harm to any child a price you’re willing to pay to keep using these systems to perhaps identify others, while we don’t know?

Is there an acceptable positive versus negative outcome rate?

The evidence so far of AI in child abuse prediction is not clearly showing that more children are helped than harmed.

Surely it’s time to stop thinking, and demand action on this.

It doesn’t take much imagination, to see the harms. Safe technology, and safe use of data, does not prevent the imagination or innovation, employed for good.

If we continue to ignore views from Patrick Brown, Ruth Gilbert, Rachel Pearson and Gene Feder, Charmaine Fletcher, Mike Stein, Tina Shaw and John Simmonds I want to know why.

Where you are willing to sacrifice certainty of human safety for the machine decision, I want someone to be accountable for why.

 

References

[1] James Ball, The European, Those waging war against reality are doomed to failure, October 4, 2018.

[2] Thanks to Graham Smith for the link. “Social Media – Guidelines on prosecuting cases involving communications sent via social media. The Crown Prosecution Service (CPS) , August 2018.”

[3] Obsuth, I., Sutherland, A., Cope, A. et al. J Youth Adolescence (2017) 46: 538. https://doi.org/10.1007/s10964-016-0468-4 London Education and Inclusion Project (LEIP): Results from a Cluster-Randomized Controlled Trial of an Intervention to Reduce School Exclusion and Antisocial Behavior (March 2016)

care.data, change, confidentiality, data, datasharing, empowerment, ethics, GDPR, National Pupil Database

Can Data Trusts be trustworthy?

The Lords Select Committee report on AI in the UK in March 2018, suggested that,“the Government plans to adopt the Hall-Pesenti Review recommendation that ‘data trusts’ be established to facilitate the ethical sharing of data between organisations.”

Since data distribution already happens, what difference would a Data Trust model make to ‘ethical sharing‘?

A ‘set of relationships underpinned by a repeatable framework, compliant with parties’ obligations’ seems little better than what we have today, with all its problems including deeply unethical policy and practice.

The ODI set out some of the characteristics Data Trusts might have or share. As importantly, we should define what Data Trusts are not. They should not simply be a new name for pooling content and a new single distribution point. Click and collect.

But is a Data Trust little more than a new description for what goes on already? Either a physical space or legal agreements for data users to pass around the personal data from the unsuspecting, and sometimes unwilling, public. Friends-with-benefits who each bring something to the party to share with the others?

As with any communal risk, it is the standards of the weakest link, the least ethical, the one that pees in the pool, that will increase reputational risk for all who take part, and spoil it for everyone.

Importantly, the Lords AI Committee report recognised that there is an inherent risk how the public would react to Data Trusts, because there is no social license for this new data sharing.

“Under the current proposals, individuals who have their personal data contained within these trusts would have no means by which they could make their views heard, or shape the decisions of these trusts.”

Views those keen on Data Trusts seem keen to ignore.

When the Administrative Data Research Network was set up in 2013, a new infrastructure for “deidentified” data linkage, extensive public dialogue was carried across across the UK. It concluded in a report with very similar findings as was apparent at dozens of care.data engagement events in 2014-15;

There is not public support for

  • “Creating large databases containing many variables/data from a large number of public sector sources,
  • Establishing greater permanency of datasets,
  • Allowing administrative data to be linked with business data, or
  • Linking of passively collected administrative data, in particular geo-location data”

The other ‘red-line’ for some participants was allowing “researchers for private companies to access data, either to deliver a public service or in order to make profit. Trust in private companies’ motivations were low.”

All of the above could be central to Data Trusts. All of the above highlight that in any new push to exploit personal data, the public must not be the last to know. And until all of the above are resolved, that social-license underpinning the work will always be missing.

Take the National Pupil Database (NPD) as a case study in a Data Trust done wrong.

It is a mega-database of over 20 other datasets. Raw data has been farmed out for years under terms and conditions to third parties, including users who hold an entire copy of the database, such as the somewhat secretive and unaccountable Fischer Family Trust, and others, who don’t answer to Freedom-of-Information, and whose terms are hidden under commercial confidentilaity. Buying and benchmarking data from schools and selling it back to some, profiling is hidden from parents and pupils, yet the FFT predictive risk scoring can shape a child’s school experience from age 2. They don’t really want to answer how staff tell if a child’s FFT profile and risk score predictions are accurate, or of they can spot errors or a wrong data input somewhere.

Even as the NPD moves towards risk reduction, its issues remain. When will children be told how data about them are used?

Is it any wonder that many people in the UK feel a resentment of institutions and orgs who feel entitled to exploit them, or nudge their behaviour, and a need to ‘take back control’?

It is naïve for those working in data policy and research to think that it does not apply to them.

We already have safe infrastructures in the UK for excellent data access. What users are missing, is the social license to do so.

Some of today’s data uses are ethically problematic.

No one should be talking about increasing access to public data, before delivering increased public understanding. Data users must get over their fear of what if the public found out.

If your data use being on the front pages would make you nervous, maybe it’s a clue you should be doing something differently. If you don’t trust the public would support it, then perhaps it doesn’t deserve to be trusted. Respect individuals’ dignity and human rights. Stop doing stupid things that undermine everything.

Build the social license that care.data was missing. Be honest. Respect our right to know, and right to object. Build them into a public UK data strategy to be understood and be proud of.

Part 1. Ethically problematic
Ethics is dissolving into little more than a buzzword. Can we find solutions underpinned by law, and ethics, and put the person first?

Part 2. Can Data Trusts be trustworthy?
As long as data users ignore data subjects rights, Data Trusts have no social license.

AI, care.data, change management, data, datasharing, engagement, ethics

Ethically problematic

Five years ago, researchers at the Manchester University School of Social Sciences wrote, “It will no longer be possible to assume that secondary data use is ethically unproblematic.”

Five years on, other people’s use of the language of data ethics puts social science at risk. Event after event, we are witnessing the gradual dissolution of the value and meaning of ‘ethics’, into little more than a buzzword.

Companies and organisations are using the language of ‘ethical’ behaviour blended with ‘corporate responsibility’ modelled after their own values, as a way to present competitive advantage.

Ethics is becoming shorthand for, ‘we’re the good guys’. It is being subverted by personal data users’ self-interest. Not to address concerns over the effects of data processing on individuals or communities, but to justify doing it anyway.

An ethics race

There’s certainly a race on for who gets to define what data ethics will mean. We have at least three new UK institutes competing for a voice in the space. Digital Catapult has formed an AI ethics committee. Data charities abound. Even Google has developed an ethical AI strategy of its own, in the wake of their Project Maven.

Lessons learned in public data policy should be clear by now. There should be no surprises how administrative data about us are used by others. We should expect fairness. Yet these basics still seem hard for some to accept.

The NHS Royal Free Hospital in 2015 was rightly criticised – because they tried “to commercialise personal confidentiality without personal consent,” as reported in Wired recently.

“The shortcomings we found were avoidable,” wrote Elizabeth Denham in 2017 when the ICO found six ways the Google DeepMind — Royal Free deal did not comply with the Data Protection Act. The price of innovation, she said, didn’t need to be the erosion of fundamental privacy rights underpinned by the law.

If the Centre for Data Ethics and Innovation is put on a statutory footing where does that leave the ICO, when their views differ?

It’s why the idea of DeepMind funding work in Ethics and Society seems incongruous to me. I wait to be proven wrong. In their own words, “technologists must take responsibility for the ethical and social impact of their work“. Breaking the law however, is conspicuous by its absence, and the Centre must not be used by companies, to generate pseudo lawful or ethical acceptability.

Do we need new digital ethics?

Admittedly, not all laws are good laws. But if recognising and acting under the authority of the rule-of-law is now an optional extra, it will undermine the ICO, sink public trust, and destroy any hope of achieving the research ambitions of UK social science.

I am not convinced there is any such thing as digital ethics. The claimed gap in an ability to get things right in this complex area, is too often after people simply get caught doing something wrong. Technologists abdicate accountability saying “we’re just developers,” and sociologists say, “we’re not tech people.”

These shrugs of the shoulders by third-parties, should not be rewarded with more data access, or new contracts. Get it wrong, get out of our data.

This lack of acceptance of responsibility creates a sense of helplessness. We can’t make it work, so let’s make the technology do more. But even the most transparent algorithms will never be accountable. People can be accountable, and it must be possible to hold leaders to account for the outcomes of their decisions.

But it shouldn’t be surprising no one wants to be held to account. The consequences of some of these data uses are catastrophic.

Accountability is the number one problem to be solved right now. It includes openness of data errors, uses, outcomes, and policy. Are commercial companies, with public sector contracts, checking data are accurate and corrected from people who the data are about, before applying in predictive tools?

Unethical practice

As Tim Harford in the FT once asked about Big Data uses in general: “Who cares about causation or sampling bias, though, when there is money to be made?”

Problem area number two, whether researchers are are working towards a profit model, or chasing grant funding is this:

How data users can make unbiased decisions whether they should use the data? We have all the same bodies deciding on data access, that oversee its governance. Conflict of self interest is built-in by default, and the allure of new data territory is tempting.

But perhaps the UK key public data ethics problem, is that the policy is currently too often about the system goal, not about improving the experience of the people using systems. Not using technology as a tool, as if people mattered. Harmful policy, can generate harmful data.

Secondary uses of data are intrinsically dependent on the ethics of the data’s operational purpose at collection. Damage-by-design is evident right now across a range of UK commercial and administrative systems. Metrics of policy success and associated data may be just wrong.

Some of the damage is done by collecting data for one purpose and using it operationally for another in secret. Until these modus operandi change no one should think that “data ethics will save us”.

Some of the most ethical research aims try to reveal these problems. But we need to also recognise not all research would be welcomed by the people the research is about, and few researchers want to talk about it. Among hundreds of already-approved university research ethics board applications I’ve read, some were desperately lacking. An organisation is no more ethical than the people who make decisions in its name. People disagree on what is morally right. People can game data input and outcomes and fail reproducibility. Markets and monopolies of power bias aims. Trying to support the next cohort of PhDs and impact for the REF, shapes priorities and values.

“Individuals turn into data, and data become regnant.” Data are often lacking in quality and completeness and given authority they do not deserve.

It is still rare to find informed discussion among the brightest and best of our leading data institutions, about the extensive everyday real world secondary data use across public authorities, including where that use may be unlawful and unethical, like buying from data brokers. Research users are pushing those boundaries for more and more without public debate. Who says what’s too far?

The only way is ethics? Where next?

The latest academic-commercial mash-ups on why we need new data ethics in a new regulatory landscape where the established is seen as past it, is a dangerous catch-all ‘get out of jail free card’.

Ethical barriers are out of step with some of today’s data politics. The law is being sidestepped and regulation diminished by lack of enforcement of gratuitous data grabs from the Internet of Things, and social media data are seen as a free-for-all. Data access barriers are unwanted. What is left to prevent harm?

I’m certain that we first need to take a step back if we are to move forward. Ethical values are founded on human rights that existed before data protection law. Fundamental human decency, rights to privacy, and to freedom from interference, common law confidentiality, tort, and professional codes of conduct on conflict of interest, and confidentiality.

Data protection law emphasises data use. But too often its first principles of necessity and proportionality are ignored. Ethical practice would ask more often, should we collect the data at all?

Although GDPR requires new necessary safeguards to ensure that technical and organisational measures are met to control and process data, and there is a clearly defined Right to Object, I am yet to see a single event thought giving this any thought.

Let’s not pretend secondary use of data is unproblematic, while uses are decided in secret. Calls for a new infrastructure actually seek workarounds of regulation. And human rights are dismissed.

Building a social license between data subjects and data users is unavoidable if use of data about people hopes to be ethical.

The lasting solutions are underpinned by law, and ethics. Accountability for risk and harm. Put the person first in all things.

We need more than hopes and dreams and talk of ethics.

We need realism if we are to get a future UK data strategy that enables human flourishing, with public support.

Notes of desperation or exasperation are increasingly evident in discourse on data policy, and start to sound little better than ‘we want more data at all costs’. If so, the true costs would be lasting.

Perhaps then it is unsurprising that there are calls for a new infrastructure to make it happen, in the form of Data Trusts. Some thoughts on that follow too.

Part 1. Ethically problematic

Ethics is dissolving into little more than a buzzword. Can we find solutions underpinned by law, and ethics, and put the person first?

Part 2. Can Data Trusts be trustworthy?

As long as data users ignore data subjects rights, Data Trusts have no social license.

Data Horizons: New Forms of Data For Social Research,

Elliot, M., Purdam, K., Mackey, E., School of Social Sciences, The University Of Manchester, CCSR Report 2013-312/6/2013