John Sheridan – The National Archives blog
This post works at two entirely different levels. It is a bold claim of right to the challenges of digital archiving, based on the longevity of the National Archives as an organisation, the trust it has earned and its commitment to its core mission – calling on a splendidly Bayesian historiography.
But it can be read another way, as an extended metaphor for government as a whole. There is the same challenge of managing modernity in long established institutions, the same need to sustain confidence during rapid systemic change. And there is the same need to grow new government services on the foundations of the old ones, drawing on the strength of old capabilities even as new ones are developed.
And that, of course, should be an unsurprising reading. Archival record keeping is changing because government itself is changing, and because archives and government both need to keep pace with the changing world.
It’s interesting to read this Economist editorial alongside Zeynep Tufekci’s TED talk. It focuses on the polarisation of political discourse driven by the persuasion architecture Tufekci describes, resulting in the politics of contempt. The argument is interesting, but perhaps doubly so when the Economist, which is not know for its histrionic rhetoric, concludes that ‘the stakes for liberal democracy could hardly be higher.’
That has implications well beyond politics and persuasion and supports the wider conclusion that algorithmic decision making needs to be understood, not just assumed to be neutral.
Zeynep Tufekci – TED
This TED talk is a little slow to get going, but increasingly catches fire. The power of algorithmically driven media may start with the crude presentation of adverts for the thing we have already just bought, but the same powers of tracking and micro-segmentation create the potential for social and political manipulation. Advertising-based social media platforms are based on persuasion architectures, and those architectures make no distinction between persuasion to buy and persuasion to vote.
That analysis leads – among other things – to a very different perception of the central risk of artificial intelligence: it is not that technology will develop a will of its own, but that it will embody, almost undetectably, the will of those in a position to use it. The technology itself may, in some senses, be neutral; the business models it supports may well not be.
Chris Yiu – Institute for Global Change
This wide ranging and fast moving report hits the Strategic Reading jackpot. It provides a bravura tour of more of the topics covered here than is plausible in a single document, ticking almost every category box along the way. It moves at considerable speed, but without sacrificing coherence or clarity. That sets the context for a set of radical recommendations to government, based on the premise established at the outset that incremental change is a route to mediocrity, that ‘status quo plus’ is a grave mistake.
Not many people could pull that off with such aplomb. The pace and fluency sweep the reader along through the recommendations, which range from the almost obvious to the distinctly unexpected. There is a debate to be had about whether they are the best (or the right) ways forward, but it’s a debate well worth having, for which this is an excellent provocation.
Richard Pope – IF
Some simple but very powerful thoughts on the intersection of automation and design. The complexity of AI, as with any other kind of complexity, cannot be allowed to get in the way of making the experience of a service simple and comprehensible. Designers have an important role to play in avoiding that risk, reinforced as the post notes by the requirement under GDPR for people to be able to understand and challenge decisions which affect them.
There is a particularly important point – often overlooked – about the need to ensure that transparency and comprehension are attributes of wider social and community networks, not just of individuals’ interaction with automated systems.
danah boyd – Point
This the transcript of a conference address, less about the weaknesses of big data a machine learning and more about its vulnerability to attack and to the encoding of systematic biases – and how everything is going to get worse. There are some worrying case studies – how easy will it turn out to be to game the software behind self-driving cars to confuse one road sign with another? – but also some hope, from turning the strength of machine learning against itself, using adversarial testing for models to probe each other’s limits. Her conclusion though is stark:
We no longer have the luxury of only thinking about the world we want to build. We must also strategically think about how others want to manipulate our systems to do harm and cause chaos.
(the preamble promises a link to a video of the whole thing, but what’s there is only one section of the piece, the rest is behind a paywall)
Martin Stewart-Weeks – Public Purpose
This is an artful piece – the first impression is of a slightly unstructured stream of consciousness, but underneath the beguilingly casual style, some great insights are pulled out, as if effortlessly. Halfway down, we are promised ‘three big ideas’, and the fulfilment does not disappoint. The one which struck home most strongly is that we design institutions not to change (or, going further still, the purpose of institutions is not to change). There is value in that – stability and persistence bring real benefits – but it’s then less surprising that those same institutions struggle to adapt to rapidly changing environments. A hint of an answer comes with the next idea: if everything is the product of a design choice, albeit sometimes an unspoken and unacknowledged one, then it is within the power of designers to make things differently.
Rachel Botsman – The Guardian
A remarkable proportion of the infrastructure of a modern state is there to compensate for the absence of trust. We need to establish identity, establish creditworthiness, have a legal system to deal with broken promises, employ police officers, security guards and locksmiths, all because we don’t know whether we can trust one another. Most of us, as it happens, are pretty trustworthy. But a few of us aren’t, and it’s really hard to work out which category each of us fall into (to say nothing of the fact that it’s partly situational, so people don’t stay neatly in one or the other).
There are some pretty obvious opportunities for big data to be brought to bear on all that, and this article focuses on a startup trying to do exactly that. That could be a tremendous way of removing friction from the way in which strangers interact, or it could be the occasion for a form of intrusive and unregulated social control (it’s not enough actually to be trustworthy, it’s essential to be able to demonstrate trustworthiness to an algorithm, with all the potential biases that brings with it) – or it could, of course, be both.
Ellen Broad – Medium
Facial recognition is the next big area where questions about data ownership, data accuracy and algorithmic bias will arise – and indeed are arising. Some of those questions have very close parallels with their equivalents in other areas of personal data, others are more distinctive – for example, discrimination against black people is endemic in poor algorithm design, but there are some very specific ways in which that manifests itself in facial recognition. This short, sharp post uses the example of a decision just made in Australia to pool driving licence pictures to create a national face recognition database to explore some of the issues around ownership, control and accountability which are of much wider relevance.
Elizabeth Churchill – EPIC
This is a long and detailed post, making two central points, one more radical and surprising than the other. The less surprising – though it certainly bears repeating – is that qualitative understanding, and particularly ethnographic understanding, is vitally important in understanding people and thus in designing systems and services. The more distinctive point is that qualitative and quantitative data are not independent of each other and more particularly that quantitative data is not neutral. Or, in the line quoted by Leisa Reichelt which led me to read the article, ‘behind every quantitative measure is a qualitative judgement imbued with a set of situated agendae’. Behind the slightly tortured language of that statement there are some important insights. One is that the interpretation of data is always something we project onto it, it is never wholly latent within it. Another – in part a corollary to the first – is that data cannot be disentangled from ethics. Taken together, that’s a reminder that the spectrum from data to knowledge is one to be traversed carefully and consciously.
Chris Weller – World Economic Forum
This is a beguiling timeline which has won a fair bit of attention for itself. It’s challenging stuff, particularly the point around 2060 when “all human tasks” will apparently be capable of being done by machines. But drawing an apparently precise timeline such as this obscures two massive sources of uncertainty. The first is the implication that people working on artificial intelligence have expertise in predicting the future of artificial intelligence. Their track record suggests that that is far from the case: like nuclear fusion, full blown AI has been twenty years in the future for decades (and the study underlying this short article strongly implies, though without ever acknowledging, that the results are as much driven by social context as by technical precision). The second is the implication that the nature of human tasks has been understood, and thus that we have some idea of what the automation of all human tasks might actually mean. There are some huge issues barely understood about that (though also something of a no true Scotsman argument – something is AI until it is achieved, at which point it is merely automation). Even if the details can be challenged, though, the trend looks clear: more activities will be more automated – and that has some critical implications, regardless of whether we choose to see it as beating humans.
Theo Bass – DECODE
The internet runs on personal data. It is the price we pay for apparently free services and for seamless integration. That’s a bargain most have been willing to make – or at least one which we feel we have no choice but to consent to. But the consequences of personal data powering the internet reverberate ever more widely, and much of the value has been captured by a small number of large companies.
That doesn’t just have the effect of making Google and Facebook very rich, it means that other potential approaches to managing – and getting value from – personal data are made harder, or even impossible. This post explores some of the challenges and opportunities that creates – and perhaps more importantly serves as an introduction to a much longer document – Me, my data and I:The future of the personal data economy – which does an excellent job both of surveying the current landscape and of telling a story about how the world might be in 2035 if ideas about decentralisation and personal control were to take hold – and what it might take to get there.
Dirk Helbing, Bruno S. Frey, Gerd Gigerenzer, Ernst Hafen, Michael Hagner, Yvonne Hofstetter, Jeroen van den Hoven, Roberto V. Zicari, Andrej Zwitter – Scientific American
There is plenty of evidence that data-driven political manipulation is on the increase, with issues getting recent coverage ranging from secretively targeted Facebook ads, bulk twitterbots and wholesale data manipulation. As with so much else, what is now possible online is an amplification of political chicanery which long pre-dates the internet – but as with so much else, the extreme difference of degree becomes a difference of kind. This portmanteau article comes at the question of whether that itself puts democracy itself under threat from a number of directions, giving it a pretty thorough examination. But there is a slight sense of technological determinism, which leads both to some sensible suggestions about how to ensure continuing personal, social and democratic control – but also to some slightly hyperbolic ideas about the threat to jobs and the imminence of super-intelligent machines,
Hila Mehr – Harvard: ASH Center for Democratic Governance and Innovation [pdf]
The first half of this paper is a slightly breathless and primarily US-focused survey of the application of AI to government – concentrating more on the present and near future, than on more distant and more speculative developments.
The second half sets out six “strategies” for making it happen, starting with the admirably dry observation that, “For many systemic reasons, government has much room for improvement when it comes to technological advancement, and AI will not solve those problems.” It’s not a bad checklist of things to keep in mind and the paper as a whole is a good straightforward introduction to the subject, but is very much an overview, not a detailed exploration.
It’s a surprisingly common mistake to design things on the assumption that they will always operate in a benign environment. But the real world is messier and more hostile than that. This is an article about why self-driving vehicles will be slower to arrive and more vulnerable once they are here than it might seem from focusing just on the enabling technology.
But it’s also an example of a much more general point. ‘What might an ill-intentioned person do to break this?’ is a question which needs to be asked early enough in designing a product or service that the answers can be addressed in the design itself, rather than, for example, ending up with medical devices which lack any kind of authentication at all. Getting the balance right between making things easy for the right people and as close to impossible as can be managed for the wrong people is never straightforward. But there is no chance of getting it right if the problem isn’t acknowledged in the first place.
And as an aside, there is still the question of whether vehicles are anywhere close to be becoming autonomous in the first place.
Simson Garfinkel – MIT Technology Review
This is a long, but fast moving and very readable, essay on why AI will arrive more slowly and do less than some of its more starry-eyed proponents assert. It’s littered with thought-provoking examples and weaves together a number of themes touched on here before – the inertial power of the installed base, the risk of confusing task completion with intelligence (and still more so general intelligence), the difference between tasks and jobs, and just how long it takes to get from proof of concept to anything close to real world practicality. There are some interesting second order thoughts as well. There is a tendency, for example, to assume that technologies (particularly digital technologies) will keep improving. But though that may well be true over a period, it’s very unlikely to be true indefinitely: in the real world, S-curves are more common than exponential growth.
A straightforward and very useful post which does exactly what the title says – a step by step explanation of what AI can now do, what it might be able to do and what there is currently no prospect of its doing (for economic as well as technical reasons).
It is – or should be – well known that 82.63% of statistics are made up. Apparent precision gives an authority to numbers which is sometimes earned, but sometimes completely spurious. More generally, this short article argues that humans have long experiences of detecting verbal nonsense, but are much less adept at spotting nonsense conveyed through numbers – and suggests a few rules of thumb for reducing the risk of being caught out.
Much of the advice offered isn’t specific to the big data of the title – but it does as an aside offer a neat encapsulation of one of the very real risks of processes based on algorithms, that of assuming that conclusions founded on data are objective and neutral, “machines are as fallible as the people who program them—and they can’t be shamed into better behaviour”.
Michelle Nijhuis – the New Yorker
Were the Beatles average? This is Matthew Taylor in good knockabout form on a spectacular failure to use data analysis to understand what takes a song to the top of the charts and, even more bravely, to construct a chart topping song. The fact that such an endeavour should fail is not surprising (though there are related questions where such analysis has been much more successful, so it’s not taste as such which is beyond the penetration of machines), but does again raise the question of whether too much attention is being given to what might be possible at the expense of making full use of what is already possible. Or as Taylor puts it, “We are currently too alarmist in thinking about technology but too timid in actually taking it up.”