“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.
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?
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.
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)
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.
Home Office reviews wrongly identified students as potentially fraudulent, yet their TOIEC visa cancellations has irrevocably damaged lives without redress.
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.
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?
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?
Time and again, thinking and discussion about these topics is siloed. At the Turing Institute, the Royal Society, the ADRN and EPSRC, in government departments, discussions on data, or within education practitioner, and public circles — we are all having similar discussions about data and ethics, but with little ownership and no goals for future outcomes. If government doesn’t get it, or have time for it, or policy lacks ethics by design, is it in the public interest for private companies, Google et al., to offer a fait accompli?
There is lots of talking about Machine Learning (ML), Artificial Intelligence (AI) and ethics. But what is being done to ensure that real values — respect for rights, human dignity, and autonomy — are built into practice in the public services delivery?
In most recent data policy it is entirely absent. The Digital Economy Act s33 risks enabling, through removal of inter and intra-departmental data protections, an unprecedented expansion of public data transfers, with “untrammelled powers”. Powers without codes of practice, promised over a year ago. That has fall out for the trustworthiness of legislative process, and data practices across public services.
Predictive analytics is growing but poorly understood in the public and public sector.
There is already dependence on computers in aspects of public sector work. Its interactions with others in sensitive situations demands better knowledge of how systems operate and can be wrong. Debt recovery, and social care to take two known examples.
Risk averse, staff appear to choose not to question the outcome of ‘algorithmic decision making’ or do not have the ability to do so. There is reportedly no analysis training for practitioners, to understand the basis or bias of conclusions. This has the potential that instead of making us more informed, decision-making by machine makes us humans less clever.
What does it do to professionals, if they feel therefore less empowered? When is that a good thing if it overrides discriminatory human decisions? How can we tell the difference and balance these risks if we don’t understand or feel able to challenge them?
In education, what is it doing to children whose attainment is profiled, predicted, and acted on to target extra or less focus from school staff, who have no ML training and without informed consent of pupils or parents?
If authorities use data in ways the public do not expect, such as to ID homes of multiple occupancy without informed consent, they will fail the future to deliver uses for good. The ‘public interest’, ‘user need,’ and ethics can come into conflict according to your point of view. The public and data protection law and ethics object to harms from use of data. This type of application has potential to be mind-blowingly invasive and reveal all sorts of other findings.
Widely informed thinking must be made into meaningful public policy for the greatest public good
Our politicians are caught up in the General Election and buried in Brexit.
Meanwhile, the commercial companies taking AI first rights to capitalise on existing commercial advantage could potentially strip public assets, use up our personal data and public trust, and leave the public with little public good. We are already used by global data players, and by machine-based learning companies, without our knowledge or consent. That knowledge can be used to profit business models, that pay little tax into the public purse.
There are valid macro economic arguments about whether private spend and investment are preferable compared with a state’s ability to do the same. But these companies make more than enough to do it all. Does it signal a failure to a commitment to the wider community; not paying just amounts of taxes, is it a red flag to a company’s commitment to public good?
What that public good should look like, depends on who is invited to participate in the room, and not to tick boxes, but to think and to build.
The Royal Society’s Report on AI and Machine Learning published on April 25, showed a working group of 14 participants, including two Google DeepMind representatives, one from Amazon, private equity investors, and academics from cognitive science and genetics backgrounds.
If we are going to form objective policies the inputs that form the basis for them must be informed, but must also be well balanced, and be seen to be balanced. Not as an add on, but be in the same room.
As Natasha Lomas in TechCrunch noted, “Public opinion is understandably a big preoccupation for the report authors — unsurprisingly so, given that a technology that potentially erodes people’s privacy and impacts their jobs risks being drastically unpopular.”
“The report also calls on researchers to consider the wider impact of their work and to receive training in recognising the ethical implications.”
What are those ethical implications? Who decides which matter most? How do we eliminate recognised discriminatory bias? What should data be used for and AI be working on at all? Who is it going to benefit? What questions are we not asking? Why are young people left out of this debate?
Who decides what the public should or should not know?
AI and ML depend on data. Data is often talked about as a panacea to problems of better working together. But data alone does not make people better informed. In the same way that they fail, if they don’t feel it is their job to pick up the fax. A fundamental building block of our future public and private prosperity is understanding data and how we, and the AI, interact. What is data telling us and how do we interpret it, and know it is accurate?
How and where will we start to educate young people about data and ML, if not about their own and use by government and commercial companies?
The whole of Chapter 5 in the report is very good as a starting point for policy makers who have not yet engaged in the area. Privacy while summed up too short in conclusions, is scattered throughout.
Blind spots remain, however.
Over willingness to accommodate existing big private players as their expertise leads design, development and a desire to ‘re-write regulation’.
Slowness to react to needed regulation in the public sector (caught up in Brexit) while commercial drivers and technology change forge ahead
‘How do we develop technology that benefits everyone’ must not only think UK, but global South, especially in the bias in how AI is being to taught, and broad socio-economic barriers in application
Predictive analytics and professional application = unwillingness to question the computer result. In children’s social care this is already having a damaging upturn in the family courts (S31)
Data and technology knowledge and ethics training, must be embedded across the public sector, not only post grad students in machine learning.
Young people are left out of discussions which, after all, are about their future. [They might have some of the best ideas, we miss at our peril.]
There is no time to waste
Children and young people have the most to lose while their education, skills, jobs market, economy, culture, care, and society goes through a series of gradual but seismic shift in purpose, culture, and acceptance before finding new norms post-Brexit. They will also gain the most if the foundations are right. One of these must be getting age verification right in GDPR, not allowing it to enable a massive data grab of child-parent privacy.
Although the RS Report considers young people in the context of a future workforce who need skills training, they are otherwise left out of this report.
“The next curriculum reform needs to consider the educational needs of young people through the lens of the implications of machine learning and associated technologies for the future of work.”
Yes it does, but it must give young people and the implications of ML broader consideration for their future, than classroom or workplace.
We are not yet talking about the effects of teaching technology to learn, and its effect on public services and interactions with the public. Questions that Sam Smith asked in Shadow of the smart machine: Will machine learning end?
At the end of this Information Age we are at a point when machine learning, AI and biotechnology are potentially life enhancing or could have catastrophic effects, if indeed “AI will cause people ‘more pain than happiness” as described by Alibaba’s founder Jack Ma.
The conflict between commercial profit and public good, what commercial companies say they will do and actually do, and fears and assurances over predicted outcomes is personified in the debate between Demis Hassabis, co-founder of DeepMind Technologies, (a London-based machine learning AI startup), and Elon Musk, discussing the perils of artificial intelligence.
Vanity Fair reported that, “Elon Musk began warning about the possibility of A.I. running amok three years ago. It probably hadn’t eased his mind when one of Hassabis’s partners in DeepMind, Shane Legg, stated flatly, “I think human extinction will probably occur, and technology will likely play a part in this.””
Musk was of the opinion that A.I. was probably humanity’s “biggest existential threat.”
We are not yet joining up multi disciplinary and cross sector discussions of threats and opportunities
Jobs, shift in needed skill sets for education, how we think, interact, value each other, accept or reject ownership and power models; and later, from the technology itself. We are not yet talking conversely, the opportunities that the seismic shifts offer in real terms. Or how and why to accept or reject or regulate them.
Where private companies are taking over personal data given in trust to public services, it is reckless for the future of public interest research to assume there is no public objection. How can we object, if not asked? How can children make an informed choice? How will public interest be assured to be put ahead of private profit? If it is intended on balance to be all about altruism from these global giants, then they must be open and accountable.
Private companies are shaping how and where we find machine learning and AI gathering data about our behaviours in our homes and public spaces.
SPACE10, an innovation hub for IKEA is currently running a survey on how the public perceives and “wants their AI to look, be, and act”, with an eye on building AI into their products, for us to bring flat-pack into our houses.
As the surveillance technology built into the Things in our homes attached to the Internet becomes more integral to daily life, authorities are now using it to gather evidence in investigations; from mobile phones, laptops, social media, smart speakers, and games. The IoT so far seems less about the benefits of collaboration, and all about the behavioural data it collects and uses to target us to sell us more things. Our behaviours tell much more than how we act. They show how we think inside the private space of our minds.
Do you want Google to know how you think and have control over that? The companies of the world that have access to massive amounts of data, and are using that data to now teach AI how to ‘think’. What is AI learning? And how much should the State see or know about how you think, or try to predict it?
Who cares, wins?
It is not overstated to say society and future public good of public services, depends on getting any co-dependencies right. As I wrote in the time of care.data, the economic value of data, personal rights and the public interest are not opposed to one another, but have synergies and co-dependency. One player getting it wrong, can create harm for all. Government must start to care about this, beyond the side effects of saving political embarrassment.
Without joining up all aspects, we cannot limit harms and make the most of benefits. There is nuance and unknowns. There is opaque decision making and secrecy, packaged in the wording of commercial sensitivity and behind it, people who can be brilliant but at the end of the day, are also, human, with all our strengths and weaknesses.
And we can get this right, if data practices get better, with joined up efforts.
Our future society, as our present, is based on webs of trust, on our social networks on- and offline, that enable business, our education, our cultural, and our interactions. Children must trust they will not be used by systems. We must build trustworthy systems that enable future digital integrity.
The immediate harm that comes from blind trust in AI companies is not their AI, but the hidden powers that commercial companies have to nudge public and policy maker behaviours and acceptance, towards private gain. Their ability and opportunity to influence regulation and future direction outweighs most others. But lack of transparency about their profit motives is concerning. Carefully staged public engagement is not real engagement but a fig leaf to show ‘the public say yes’.
The unwillingness by Google DeepMind, when asked at their public engagement event, to discuss their past use of NHS patient data, or the profit model plan or their terms of NHS deals with London hospitals, should be a warning that these questions need answers and accountability urgently.
Companies that have already extracted and benefited from personal data in the public sector, have already made private profit. They and their machines have learned for their future business product development.
A transparent accountable future for all players, private and public, using public data is a necessary requirement for both the public good and private profit. It is not acceptable for departments to hide their practices, just as it is unacceptable if firms refuse algorithmic transparency.
“Rebooting antitrust for the information age will not be easy. It will entail new risks: more data sharing, for instance, could threaten privacy. But if governments don’t want a data economy dominated by a few giants, they will need to act soon.” [The Economist, May 6]
If the State creates a single data source of truth, or private Giant tech thinks it can side-step regulation and gets it wrong, their practices screw up public trust. It harms public interest research, and with it our future public good.
But will they care?
If we care, then across public and private sectors, we must cherish shared values and better collaboration. Embed ethical human values into development, design and policy. Ensure transparency of where, how, who and why my personal data has gone.
We must ensure that as the future becomes “smarter”, we educate ourselves and our children to stay intelligent about how we use data and AI.
We must start today, knowing how we are used by both machines, and man.
Is Education preparing us for the jobs of the future?
The panel talked about changing social and political realities. We considered the effects on employment. We began discussion how those changes should feed into education policy and practice today. It is discussion that should be had by the public. So far, almost a year after the Referendum, the UK government is yet to say what post-Brexit Britain might look like. Without a vision, any mandate for the unknown, if voted for on June 9th, will be meaningless.
What was talked about and what should be a public debate:
What jobs will be needed in the future?
Post Brexit, what skills will we need in the UK?
How can the education system adapt and improve to help future generations develop skills in this ever changing landscape?
How do we ensure women [and anyone else] are not left behind?
Brexit is the biggest change management project I may never see.
As the State continues making and remaking laws, reforming education, and starts exiting the EU, all in parallel, technology and commercial companies won’t wait to see what the post-Brexit Britain will look like. In our state’s absence of vision, companies are shaping policy and ‘re-writing’ their own version of regulations. What implications could this have for long term public good?
What will be needed in the UK future?
A couple of sentences from Alan Penn have stuck with me all week. Loosely quoted, we’re seeing cultural identity shift across the country, due to the change of our available employment types. Traditional industries once ran in a family, with a strong sense of heritage. New jobs don’t offer that. It leaves a gap we cannot fill with “I’m a call centre worker”. And this change is unevenly felt.
There is no tangible public plan in the Digital Strategy for dealing with that change in the coming 10 to 20 years employment market and what it means tied into education. It matters when many believe, as do these authors in American Scientific, “around half of today’s jobs will be threatened by algorithms. 40% of today’s top 500 companies will have vanished in a decade.”
So what needs thought?
Analysis of what that regional jobs market might look like, should be a public part of the Brexit debate and these elections →
We need to see those goals, to ensure policy can be planned for education and benchmark its progress towards achieving its aims
Brexit and technology will disproportionately affect different segments of the jobs market and therefore the population by age, by region, by socio-economic factors →
Education policy must therefore address aspects of skills looking to the future towards employment in that new environment, so that we make the most of opportunities, and mitigate the harms.
Brexit and technology will disproportionately affect communities → What will be done to prevent social collapse in regions hardest hit by change?
Where are we starting from today?
Before we can understand the impact of change, we need to understand what the present looks like. I cannot find a map of what the English education system looks like. No one I ask seems to have one or have a firm grasp across the sector, of how and where all the parts of England’s education system fit together, or their oversight and accountability. Everyone has an idea, but no one can join the dots. If you have, please let me know.
Nothing is constant in education like change; in laws, policy and its effects in practice, so I shall start there.
1. Legislation
In retrospect it was a fatal flaw, missed in post-Referendum battles of who wrote what on the side of a bus, that no one did an assessment of education [and indeed other] ‘legislation in progress’. There should have been recommendations made on scrapping inappropriate government bills in entirety or in parts. New laws are now being enacted, rushed through in wash up, that are geared to our old status quo, and we risk basing policy only on what we know from the past, because on that, we have data.
In the timeframe that Brexit will become tangible, we will feel the effects of the greatest shake up of Higher Education in 25 years. Parts of the Higher Education and Research Act, and Technical and Further Education Act are unsuited to the new order post-Brexit.
What it will do: The new HE law encourages competition between institutions, and the TFE Act centred in large part on how to manage insolvency.
What it should do: Policy needs to promote open, collaborative networks if within a now reduced research and academic circle, scholarly communities are to thrive.
Legislation has recently not only meant restructure, but repurposing of what education [authorities] is expected to offer.
A new Statutory Instrument — The School and Early Years Finance (England) Regulations 2017 — makes music, arts and playgrounds items; ‘That may be removed from maintained schools’ budget shares’.
How will this withdrawal of provision affect skills starting from the Early Years throughout young people’s education?
2. Policy
Education policy if it continues along the grammar school path, will divide communities into ‘passed’ and the ‘unselected’. A side effect of selective schooling— a feature or a bug dependent on your point of view — is socio-economic engineering. It builds class walls in the classroom, while others, like Fabian Women, say we should be breaking through glass ceilings. Current policy in a wider sense, is creating an environment that is hostile to human integration. It creates division across the entire education system for children aged 2–19.
The curriculum is narrowing, according to staff I’ve spoken to recently, as a result of measurement focus on Progress 8, and due to funding constraints.
What effect will this have on analysis of knowledge, discernment, how to assess when computers have made a mistake or supplied misinformation, and how to apply wisdom? Skills that today still distinguish human from machine learning.
What narrowing the curriculum does: Students have fewer opportunities to discover their skill set, limiting opportunities for developing social skills and cultural development, and their development as rounded, happy, human beings.
What we could do: Promote long term love of learning in-and-outside school and in communities. Reinvest in the arts, music and play, which support mental and physical health and create a culture in which people like to live as well as work. Library and community centres funding must be re-prioritised, ensuring inclusion and provision outside school for all abilities.
Austerity builds barriers of access to opportunity and skills. Children who cannot afford to, are excluded from extra curricular classes. We already divide our children through private and state education, into those who have better facilities and funding to enjoy and explore a fully rounded education, and those whose funding will not stretch much beyond the bare curriculum. For SEN children, that has already been stripped back further.
Existing barriers are likely to become entrenched in twenty years. What does it do to society, if we are divided in our communities by money, or gender, or race, and feel disempowered as individuals? Are we less responsible for our actions if there’s nothing we can do about it? If others have more money, more power than us, others have more control over our lives, and “no matter what we do, we won’t pass the 11 plus”?
Without joined-up scrutiny of these policy effects across the board, we risk embedding these barriers into future planning. Today’s data are used to train “how the system should work”. If current data are what applicants in 5 years will base future expectations on, will their decisions be objective and will in-built bias be transparent?
3. Sociological effects of legislation.
It’s not only institutions that will lose autonomy in the Higher Education and Research Act.
At present, the risk to the autonomy of science and research is theoretical — but the implications for academic freedom are troubling. [Nature 538, 5 (06 October 2016)]
The Secretary of State for Education now also has new Powers of Information about individual applicants and students. Combined with the Digital Economy Act, the law can ride roughshod over students’ autonomy and consent choices. Today they can opt out of UCAS automatically sharing their personal data with the Student Loans Company for example. Thanks to these new powers, and combined with the Digital Economy Act, that’s gone.
The Act further includes the intention to make institutions release more data about course intake and results under the banner of ‘transparency’. Part of the aim is indisputably positive, to expose discrimination and inequality of all kinds. It also aims to make the £ cost-benefit return “clearer” to applicants — by showing what exams you need to get in, what you come out with, and then by joining all that personal data to the longitudinal school record, tax and welfare data, you see what the return is on your student loan. The government can also then see what your education ‘cost or benefit’ the Treasury. It is all of course much more nuanced than that, but that’s the very simplified gist.
This ‘destinations data’ is going to be a dataset we hear ever more about and has the potential to influence education policy from age 2.
Aside from the issue of personal data disclosiveness when published by institutions — we already know of individuals who could spot themselves in a current published dataset — I worry that this direction using data for ‘advice’ is unhelpful. What if we’re looking at the wrong data upon which to base future decisions? The past doesn’t take account of Brexit or enable applicants to do so.
Researchers [and applicants, the year before they apply or start a course] will be looking at what *was* — predicted and achieved qualifying grades, make up of the class, course results, first job earnings — what was for other people, is at least 5 years old by the time it’s looked at it. Five years is a long time out of date.
4. Change
Teachers and schools have long since reached saturation point in the last 5 years to handle change. Reform has been drastic, in structures, curriculum, and ongoing in funding. There is no ongoing teacher training, and lack of CPD take up, is exacerbated by underfunding.
Teachers are fed up with change. They want stability. But contrary to the current “strong and stable” message, reality is that ahead we will get anything but, and must instead manage change if we are to thrive. Politically, we will see backlash when ‘stable’ is undeliverable.
But Teaching has not seen ‘stable’ for some time. Teachers are asking for fewer children, and more cash in the classroom. Unions talk of a focus on learning, not testing, to drive school standards. If the planned restructuring of funding happens, how will it affect staff retention?
We know schools are already reducing staff. How will this affect employment, adult and children’s skill development, their ambition, and society and economy?
Where could legislation and policy look ahead?
What are the big Brexit targets and barriers and when do we expect them?
How is the fall out from underfunding and reduction of teaching staff expected to affect skills provision?
State education policy is increasingly hands-off. What is the incentive for local schools or MATs to look much beyond the short term?
How do local decisions ensure education is preparing their community, but also considering society, health and (elderly) social care, Post-Brexit readiness and women’s economic empowerment?
How does our ageing population shift in the same time frame?
How can the education system adapt?
We need to talk more about other changes in the system in parallel to Brexit; join the dots, plus the potential positive and harmful effects of technology.
Gender here too plays a role, as does mitigating discrimination of all kinds, confirmation bias, and even in the tech itself, whether AI for example, is going to be better than us at decision-making, if we teach AI to be biased.
Dr Lisa Maria Mueller talked about the effects and influence of age, setting and language factors on what skills we will need, and employment. While there are certain skills sets that computers are and will be better at than people, she argued society also needs to continue to cultivate human skills in cultural sensitivities, empathy, and understanding. We all nodded. But how?
To develop all these human skills is going to take investment. Investment in the humans that teach us. Bennie Kara, Assistant Headteacher in London, spoke about school cuts and how they will affect children’s futures.
The future of England’s education must be geared to a world in which knowledge and facts are ubiquitous, and readily available online than at any other time. And access to learning must be inclusive. That means including SEN and low income families, the unskilled, everyone. As we become more internationally remote, we must put safeguards in place if we to support thriving communities.
Policy and legislation must also preserve and respect human dignity in a changing work environment, and review not only what work is on offer, but *how*; the kinds of contracts and jobs available.
Where might practice need to adapt now?
Re-consider curriculum content with its focus on facts. Will success risk being measured based on out of date knowledge, and a measure of recall? Are these skills in growing or dwindling need?
Knowledge focus must place value on analysis, discernment, and application of facts that computers will learn and recall better than us. Much of that learning happens outside school.
Opportunities have been cut, together with funding. We need communities brought back together, if they are not to collapse. Funding centres of local learning, restoring libraries and community centres will be essential to local skill development.
What is missing?
Although Sarah Waite spoke (in a suitably Purdah appropriate tone), about the importance of basic skills in the future labour market we didn’t get to talking about education preparing us for the lack of jobs of the future and what that changed labour market will look like.
What skills will *not* be needed? Who decides? If left to companies’ sponsor led steer in academies, what effects will we see in society?
Discussions of a future education model and technology seem to share a common theme: people seem reduced in making autonomous choices. But they share no positive vision.
Technology should empower us, but it seems to empower the State and diminish citizens’ autonomy in many of today’s policies, and in future scenarios especially around the use of personal data and Digital Economy.
Technology should enable greater collaboration, but current tech in education policy is focused too little on use on children’s own terms, and too heavily on top-down monitoring: of scoring, screen time, search terms. Further restrictions by Age Verification are coming, and may access and reduce participation in online services if not done well.
Infrastructure weakness is letting down the skill training: University Technical Colleges (UTCs) are not popular and failing to fill places. There is lack of an overarching area wide strategic plan for pupils in which UTCS play a part. Local Authorities played an important part in regional planning which needs restored to ensure joined up local thinking.
How do we ensure women are not left behind?
The final question of the evening asked how women will be affected by Brexit and changing job market. Part of the risks overall, the panel concluded, is related to [lack of] equal-pay. But where are the assessments of the gendered effects in the UK of:
community structural change and intra-family support and effect on demand for social care
tech solutions in response to lack of human interaction and staffing shortages including robots in the home and telecare
the disproportionate drop out of work, due to unpaid care roles, and difficulty getting back in after a break.
the roles and types of work likely to be most affected or replaced by machine learning and robots
and how will women be empowered or not socially by technology?
We quickly need in education to respond to the known data where women are already being left behind now. The attrition rate for example in teaching in England after two-three years is poor, and getting worse. What will government do to keep teachers teaching? Their value as role models is not captured in pupils’ exams results based entirely on knowledge transfer.
Our GCSEs this year go back to pure exam based testing, and remove applied coursework marking, and is likely to see lower attainment for girls than boys, say practitioners. Likely to leave girls behind at an earlier age.
“There is compelling evidence to suggest that girls in particular may be affected by the changes — as research suggests that boys perform more confidently when assessed by exams alone.”
Jennifer Tuckett spoke about what fairness might look like for female education in the Creative Industries. From school-leaver to returning mother, and retraining older women, appreciating the effects of gender in education is intrinsic to the future jobs market.
We also need broader public understanding of the loop of the impacts of technology, on the process and delivery of teaching itself, and as school management becomes increasingly important and is male dominated, how will changes in teaching affect women disproportionately? Fact delivery and testing can be done by machine, and supports current policy direction, but can a computer create a love of learning and teach humans how to think?
“There is a opportunity for a holistic synthesis of research into gender, the effect of tech on the workplace, the effect of technology on care roles, risks and opportunities.”
Delivering education to ensure women are not left behind, includes avoiding women going into education as teenagers now, to be led down routes without thinking of what they want and need in future. Regardless of work.
Education must adapt to changed employment markets, and the social and community effects of Brexit. If it does not, barriers will become embedded. Geographical, economic, language, familial, skills, and social exclusion.
In short
In summary, what is the government’s Brexit vision? We must know what they see five, 10, and for 25 years ahead, set against understanding the landscape as-is, in order to peg other policy to it.
With this foundation, what we know and what we estimate we don’t know yet can be planned for.
Once we know where we are going in policy, we can do a fit-gap to map how to get people there.
Estimate which skills gaps need filled and which do not. Where will change be hardest?
Change is not new. But there is current potential for massive long term economic and social lasting damage to our young people today. Government is hindered by short term political thinking, but it has a long-term responsibility to ensure children are not mis-educated because policy and the future environment are not aligned.
We deserve public, transparent, informed debate to plan our lives.
We enter the unknown of the education triangle at our peril; Brexit, underfunding, divisive structural policy, for the next ten years and beyond, without appropriate adjustment to pre-Brexit legislation and policy plans for the new world order.
The combined negative effects on employment at scale and at pace must be assessed with urgency, not by big Tech who will profit, but with an eye on future fairness, and public economic and social good. Academy sponsors, decision makers in curriculum choices, schools with limited funding, have no incentives to look to the wider world.
If we’re going to go it alone, we’d be better be robust as a society, and that can’t be just some of us, and can’t only be about skills as seen as having an tangible output.
All this discussion is framed by the premise that education’s aim is to prepare a future workforce for work, and that it is sustainable.
Policy is increasingly based on work that is measured by economic output. We must not leave out or behind those who do not, or cannot, or whose work is unmeasured yet contributes to the world.
‘The only future worth building includes everyone,’ said the Pope in a recent TedTalk.
What kind of future do you want to see yourself living in? Will we all work or will there be universal basic income? What will happen on housing, an ageing population, air pollution, prisons, free movement, migration, and health? What will keep communities together as their known world in employment, and family life, and support collapse? How will education enable children to discover their talents and passions?
Human beings are more than what we do. The sense of a country of who we are and what we stand for is about more than our employment or what we earn. And we cannot live on slogans alone.
Who do we think we in the UK will be after Brexit, needs real and substantial answers. What are we going to *do* and *be* in the world?
Without this vision, any mandate as voted for on June 9th, will be made in the dark and open to future objection writ large. ‘We’ must be inclusive based on a consensus, not simply a ‘mandate’.
Only with clear vision for all these facets fitting together in a model of how we will grow in all senses, will we be able to answer the question, is education preparing us [all] for the jobs of the future?
More than this, we must ask if education is preparing people for the lack of jobs, for changing relationships in our communities, with each other, and with machines.
Change is coming, Brexit or not. But Brexit has exacerbated the potential to miss opportunities, embed barriers, and see negative side-effects from changes already underway in employment, in an accelerated timeframe.
If our education policy today is not gearing up to that change, we must.
Notes and thoughts from Full Fact’s event at Newspeak House in London on 27/3 to discuss fake news, the misinformation ecosystem, and how best to respond. The recording is here. The contributions and questions part of the evening began from 55.55.
What is fake news? Are there solutions?
1. Clickbait: celebrity pull to draw online site visitors towards traffic to an advertising model – kill the business model
2. Mischief makers: Deceptive with hostile intent – bots, trolls, with an agenda
3. Incorrectly held views: ‘vaccinations cause autism’ despite the evidence to the contrary. How can facts reach people who only believe what they want to believe?
Why does it matter? The scrutiny of people in power matters – to politicians, charities, think tanks – as well as the public.
It is fundamental to remember that we do in general believe that the public has a sense of discernment, however there is also a disconnect between an objective truth and some people’s perception of reality. Can this conflict be resolved? Is it necessary to do so? If yes, when is it necessary to do so and who decides that?
There is a role for independent tracing of unreliable information, its sources and its distribution patterns and identifying who continues to circulate fake news even when asked to desist.
Transparency about these processes is in the public interest.
Overall, there is too little public understanding of how technology and online tools affect behaviours and decision-making.
The Role of Media in Society
How do you define the media?
How can average news consumers distinguish between self-made and distributed content compared with established news sources?
What is the role of media in a democracy?
What is the mainstream media?
Does the media really represent what I want to understand? > Does the media play a role in failure of democracy if news is not representative of all views? > see Brexit, see Trump
What are news values and do we have common press ethics?
New problems in the current press model:
Failure of the traditional media organisations in fact checking; part of the problem is that the credible media is under incredible pressure to compete to gain advertising money share.
Journalism is under resourced. Verification skills are lacking and tools can be time consuming. Techniques like reverse image search, and verification take effort.
Press releases with numbers can be less easily scrutinised so how do we ensure there is not misinformation through poor journalism?
What about confirmation bias and reinforcement?
What about friends’ behaviours? Can and should we try to break these links if we are not getting a fair picture? The Facebook representative was keen to push responsibility for the bubble entirely to users’ choices. Is this fair given the opacity of the model?
Have we cracked the bubble of self-reinforcing stories being the only stories that mutual friends see?
Can we crack the echo chamber?
How do we start to change behaviours? Can we? Should we?
The risk is that if people start to feel nothing is trustworthy, we trust nothing. This harms relations between citizens and state, organisations and consumers, professionals and public and between us all. Community is built on relationships. Relationships are built on trust. Trust is fundamental to a functioning society and economy.
Is it game over?
Will Moy assured the audience that there is no need to descend into blind panic and there is still discernment among the public.
Then, it was asked, is perhaps part of the problem that the Internet is incapable in its current construct to keep this problem at bay? Is part of the solution re-architecturing and re-engineering the web?
What about algorithms? Search engines start with word frequency and neutral decisions but are now much more nuanced and complex. We really must see how systems decide what is published. Search engines provide but also restrict our access to facts and ‘no one gets past page 2 of search results’. Lack of algorithmic transparency is an issue, but will not be solved due to commercial sensitivities.
Fake news creation can be lucrative. Mangement models that rely on user moderation or comments to give balance can be gamed.
Are there appropriate responses to the grey area between trolling and deliberate deception through fake news that is damaging? In what context and background? Are all communities treated equally?
The question came from the audience whether the panel thought regulation would come from the select committee inquiry. The general response was that it was unlikely.
What are the solutions?
The questions I came away thinking about went unanswered, because I am not sure there are solutions as long as the current news model exists and is funded in the current way by current players.
I believe one of the things that permits fake news is the growing imbalance of money between the big global news distributors and independent and public interest news sources.
This loss of balance, reduces our ability to decide for ourselves what we believe and what matters to us.
The monetisation of news through its packaging in between advertising has surely contaminated the news content itself.
Think of a Facebook promoted post – you can personalise your audience to a set of very narrow and selective characteristics. The bubble that receives that news is already likely to be connected by similar interest pages and friends and the story becomes self reinforcing, showing up in friends’ timelines.
A modern online newsroom moves content on the webpage around according to what is getting the most views and trending topics in a list encourage the viewers to see what other people are reading, and again, are self reinforcing.
There is also a lack of transparency of power. Where we see a range of choices from which we may choose to digest a range of news, we often fail to see one conglomerate funder which manages them all.
The discussion didn’t address at all the fundamental shift in “what is news” which has taken place over the last twenty years. In part, I believe the responsibility for the credibility level of fake news in viewers lies with 24/7 news channels. They have shifted the balance of content from factual bulletins, to discussion and opinion. Now while the news channel is seen as a source of ‘news’ much of the time, the content is not factual, but opinion, and often that means the promotion and discussion of the opinions of their paymaster.
Most simply, how should I answer the question that my ten year old asks – how do I know if something on the Internet is true or not?
Can we really say it is up to the public to each take on this role and where do we fit the needs of the vulnerable or children into that?
Is the term fake news the wrong approach and something to move away from? Can we move solutions away from target-fixation ‘stop fake news’ which is impossible online, but towards what the problems are that fake news cause?
Interference in democracy. Interference in purchasing power. Interference in decision making. Interference in our emotions.
These interferences with our autonomy is not something that the web is responsible for, but the people behind the platforms must be accountable for how their technology works.
In the mean time, what can we do?
“if we ever want the spread of fake news to stop we have to take responsibility for calling out those who share fake news (real fake news, not just things that feel wrong), and start doing a bit of basic fact-checking ourselves.” [IB Times, Eliot Higgins is the founder of Bellingcat]
Not everyone has the time or capacity to each do that. As long as today’s imbalance of money and power exists, truly independent organisations like Bellingcat and FullFact have an untold value.
The billed Google and Twitter speakers were absent because they were invited to a meeting with the Home Secretary on 28/3. Speakers were Will Moy, Director of FullFact, Jenni Sargent Managing Director of firstdraftnews, Richard Allan, Facebook EMEA Policy Director and the event was chaired by Bill Thompson.
In this post, I think out loud about what improving online safety for children in The Green Paper on Children’s Internet Safety means ahead of the General Data Protection Regulation in 2018. Children should be able to use online services without being used and abused by them. If this regulation and other UK Government policy and strategy are to be meaningful for children, I think we need to completely rethink the State approach to what data privacy means in the Internet of Things.
[listen on soundcloud]
Children in the Internet of Things
In 1979 Star Trek: The Motion Picture created a striking image of A.I. as Commander Decker merged with V’Ger and the artificial copy of Lieutenant Ilia, blending human and computer intelligence and creating an integrated, synthesised form of life.
Ten years later, Sir Tim Berners-Lee wrote his proposal and created the world wide web, designing the way for people to share and access knowledge with each other through networks of computers.
In the 90s my parents described using the Internet as spending time ‘on the computer’, and going online meant from a fixed phone point.
Today our wireless computers in our homes, pockets and school bags, have built-in added functionality to enable us to do other things with them at the same time; make toast, play a game, and make a phone call, and we live in the Internet of Things.
Although we talk about it as if it were an environment of inanimate appliances, it would be more accurate to think of the interconnected web of information that these things capture, create and share about our interactions 24/7, as vibrant snapshots of our lives, labelled with retrievable tags, and stored within the Internet.
Data about every moment of how and when we use an appliance, is captured at a rapid rate, or measured by smart meters, and shared within a network of computers. Computers that not only capture data but create, analyse and exchange new data about the people using them and how they interact with the appliance.
In this environment, children’s lives in the Internet of Things no longer involve a conscious choice to go online. Using the Internet is no longer about going online, but being online. The web knows us. In using the web, we become part of the web.
Our children, to the computers that gather their data, have simply become extensions of the things they use about which data is gathered and sold by the companies who make and sell the things. Things whose makers can even choose who uses them or not and how. In the Internet of things, children have become things of the Internet.
A child’s use of a smart hairbrush will become part of the company’s knowledge base how the hairbrush works. A child’s voice is captured and becomes part of the database for the development training of the doll or robot they play with.
Our biometrics, measurements of the unique physical parts of our identities, provides a further example of the recent offline-self physically incorporated into banking services. Over 1 million UK children’s biometrics are estimated to be used in school canteens and library services through, often compulsory, fingerprinting.
Our interactions create a blended identity of online and offline attributes.
The web has created synthesised versions of our selves.
I say synthesised not synthetic, because our online self is blended with our real self and ‘synthetic’ gives the impression of being less real. If you take my own children’s everyday life as an example, there is no ‘real’ life that is without a digital self. The two are inseparable. And we might have multiple versions.
Our synthesised self is not only about our interactions with appliances and what we do, but who we know and how we think based on how we take decisions.
Data is created and captured not only about how we live, but where we live. These online data can be further linked with data about our behaviours offline generated from trillions of sensors and physical network interactions with our portable devices. Our synthesised self is tracked from real life geolocations. In cities surrounded by sensors under pavements, in buildings, cameras, mapping and tracking everywhere we go, our behaviours are converted into data, and stored inside an overarching network of cloud computers so that our online lives take on life of their own.
Data about us, whether uniquely identifiable on its own or not, is created and collected actively and passively. Online site visits record IP Address and use linked platform log-ins that can even extract friends lists without consent or affirmative action from them.
Using a tool like Privacy Badger from EEF gives you some insight into how many sites create new data about online behaviour once that synthesised self logs in, then tracks your synthesised self across the Internet. How you move from page to page, with what referring and exit pages and URLs, what adverts you click on or ignore, platform types, number of clicks, cookies, invisible on page gifs and web beacons. Data that computers see, interpret and act on better than us.
Those synthesised identities are tracked online, just as we move about a shopping mall offline.
Sir Tim Berners-Lee said this week, there is a need to put “a fair level of data control back in the hands of people.” It is not a need but vital to our future flourishing, very survival even. Data control is not about protecting a list of information or facts about ourselves and our identity for its own sake, it is about choosing who can exert influence and control over our life, our choices, and future of democracy.
We get the service, the web gets our identity and our behaviours. And in what is in effect a hidden slave trade, they get access to use our synthesised selves in secret, and forever.
This grasp of what the Internet is, what the web is, is key to getting a rounded view of children’s online safety. Namely, we need to get away from the sole focus of online safeguarding as about children’s use of the web, and also look at how the web uses children.
Online services use children to:
mine, and exchange, repackage, and trade profile data, offline behavioural data (location, likes), and invisible Internet-use behavioural data (cookies, website analytics)
extend marketing influence in human decision-making earlier in life, even before children carry payment cards of their own,
enjoy the insights of parent-child relationships connected by an email account, sometimes a credit card, used as age verification or in online payments.
What are the risks?
Exploitation of identity and behavioural tracking not only puts our synthesised child at risk from exploitation, it puts our real life child’s future adult identity and data integrity at risk. If we cannot know who holds the keys to our digital identity, how can we trust that systems and services will be fair to us, not discriminate or defraud. Or not make errors that we cannot understand in order to correct?
Leaks, loss and hacks abound and manufacturers are slow to respond. Software that monitors children can also be used in coercive control. Organisations whose data are insecure, can be held to ransom. Children’s products should do what we expect them to and nothing more, there should be “no surprises” how data are used.
Companies tailor and target their marketing activity to those identity profiles. Our data is sold on in secret without consent to data brokers we never see, who in turn sell us on to others who monitor, track and target our synthesised selves every time we show up at their sites, in a never-ending cycle.
And from exploiting the knowledge of our synthesised self, decisions are made by companies, that target their audience, select which search results or adverts to show us, or hide, on which network sites, how often, to actively nudge our behaviours quite invisibly.
Nudge misuse is one of the greatest threats to our autonomy and with it democratic control of the society we live in. Who decides on the “choice architecture” that may shape another’s decisions and actions, and on what ethical basis? once asked these authors who now seem to want to be the decision makers.
Loss of identity is near impossible to reclaim. Our synthesised selves are sold into unending data slavery and we seem powerless to stop it. Our autonomy and with it our self worth, seem diminished.
How can we protect children better online?
Safeguarding must include ending data slavery of our synthesised self. I think of five things needed by policy shapers to tackle it.
Understanding what ‘online’ and the Internet mean and how the web works – i.e. what data does a visit to a web page collect about the user and what happens to that data?
Threat models and risk must go beyond the usual irl protection issues. Those posed by undermining citizens’ autonomy, loss of public trust, of control over our identity, misuse of nudge, and how some are intrinsic to the current web business model, site users or government policy are unseen are underestimated.
On user regulation (age verification / filtering) we must confront the idea that as a stand-alone step it will not create a better online experience for the user, when it will not prevent the misuse of our synthesised selves and may increase risks – regulation of misuse must shift the point of responsibility
Meaningful data privacy training must be mandatory for anyone in contact with children and its role in children’s safeguarding
Siloed thinking must go. Forward thinking must join the dots across Departments into cohesive inclusive digital strategy and that doesn’t just mean ‘let’s join all of the data, all of the time’
Respect our synthesised selves. Data slavery includes government misuse and must end if we respect children’s rights.
In the words of James T. Kirk, “the human adventure is just beginning.”
When our synthesised self is an inseparable blend of offline and online identity, every child is a synthesised child. And they are people. It is vital that government realises their obligation to protect rights to privacy, provision and participation under the Convention of the Rights of the Child and address our children’s real online life.
Governments, policy makers, and commercial companies must not use children’s offline safety as an excuse in a binary trade off to infringe on those digital rights or ignore risk and harm to the synthesised self in law, policy, and practice.
If future society is to thrive we must do all that is technologically possible to safeguard the best of what makes us human in this blend; our free will.
Part 2 follows with thoughts specific to the upcoming regulations, Digital Economy Bill andDigital Strategy
“What do an umbrella, a shark, a houseplant, the brake pads in a mining truck and a smoke detector all have in common? They can all be connected online, and in this example, in this WEF film, they are.
“By 2024 more than 50% of home Internet traffic will be used by appliances and devices, rather than just for communication and entertainment…The IoT raises huge questions on privacy and security, that have to be addressed by government, corporations and consumers.”
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.
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).
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] OliviaVarley-Winter, HetanShah, ‘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.
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?
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.
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.
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.
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.
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.
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)
