The user interface of a toaster is an unlikely starting place for an essay about the nature of design, but it turns out to be a way into a discussion of some challenging questions, including whether design is an exercise in democracy, an act of genius, or something else altogether. The conclusion is that the purpose of design is to make things more the essence of themselves, and that in this example, at least, it is to identify an ‘obvious but still unseen problem’ and to solve that problem in ‘in an elegant way that is also delightful to use’.
That risks collapsing into triteness, but the underlying question is a good one. Toasters have been around – fundamentally unchanged – for a century. So radically changing how they work (while at the same time barely changing how they work at all) is not a trivial achievement. It’s worth reflecting both on what lessons that might have for the application of design to public services – and on what obvious but unseen problems are waiting to be found.
Ian Bogost – The Atlantic
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).
This is not a post which self-evidently belongs in Strategic Reading: it’s about how a software developer should work with her boss to get things done. But the suggested approach has two devastatingly simple elements which have much wider resonance, first that understanding the position and priorities of the organisation as a whole is important in addressing even what might appear to be quite small problems; secondly that based on that foundation, prioritisation can and should be a neat constant activity, driven by the need to manage down uncertainty. It’s worth reflecting on where else these ideas might provide and effective way of getting good things done.
Dan Milstein – Hut 8 Labs
Most of what appears on Strategic Reading is gleaned from social media in one way or another. This is the first which comes from a direct reader suggestion – so thanks to Stephen Gill. Interesting links are welcome, either through the site itself or by tweeting to @StratRead.
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
All the signs that you would expect to see in the labour market and wider economy if robots were displacing jobs are absent: productivity is not growing rapidly, labour turnover is not going up, and employment remains high.
That’s not to say, of course, that automation isn’t happening – and Surowiecki is careful not to say it – or that what has happened up to now is an infallible guide to what will happen in the future. But this article does contribute to the recognition that technological progress, the social and economic adoption of that progress, and the wider impact of that adoption are all very different things, potentially with very significant lags between them. That perspective is now coming through more strongly elsewhere as well – which should mean that the debate can be more balanced.
James Surowiecki – Wired
The success of Amazon has been told many times and in many ways. This is one of the less obvious and more compelling versions, focusing on the power of treating its internal systems and relationships as if they were external If they are good enough for other people to want to use them, that’s a good sign that they are good enough for Amazon to use for themselves.
It’s clearly worked pretty powerfully for Amazon. That’s interesting in its own right, but it also raises some important and difficult questions for organisations which are not Amazon, perhaps in particular for governments, which are quite heavily insulated from the consequences of customer satisfaction. Government is not the next Amazon, nor should it be, but it’s worth reflecting on whether there is a similar process by which a drive to quality improvement could be designed into processes and systems.
Zack Kanter – Tech Crunch
The people with the shiny ideas and the shiny kit can see that change is essential and just know that their ideas are right as well as shiny. Unaccountably, the less shiny people with the unfashionable kit fail immediately to see the inherent rightness of the cause. This post has the superficial form of a rant, but it is a rant based on some important observations and a question without an easy answer: how do transformation teams understand and address the user needs of those whose fate is to be transformed?
Alex Blandford – Medium
Organisational transformation is a very big and very difficult problem. We tell each other stories of transformations which have failed, or fallen well short of their ambition, much more often that we find stories to tell of triumphant success. This post doesn’t attempt to contribute to the grand theory of organisational change, but presents a very simple (which is not at all the same as easy) list of ten practical ways of improving the chances of success.
Sue Visic – ThoughtWorks
When demand fluctuates, the costs of meeting it can easily go up and its quality can quickly go down. Some public services seem designed to maximise peak demand, others need to be ready to respond to demand which fluctuates for external reasons. That’s an aspect of service design which is often overlooked: the need to optimise individual interactions and the system supporting them is much more obvious than the opportunity to influence (and in some cases directly manage) the pattern of activity over time. So it’s worth reflecting on the scope for improving both service quality and efficiency by better managing the flow of demand.
Eddie Copeland – NESTA
How mad should you be to work here?
Ben Holliday is leaving the mad hatter’s tea party of government, worrying in his valedictory post about the risk that he was starting to go native, to see the madness as normality. That’s a good concern to have (and retaining a sense of alienness is a much underrated skill), but there is a reverse danger too: not being aware enough of the constraints and opportunities given by organisations (and their cultures and contexts) brings a real risk that even the best intended change fails to deliver its potential.
There is a sweet spot to be found – having enough experience and understanding to be effective in a particular environment, but not so much as to fall into the trap of thinking that everything is as it can only be. Alice may have left the tea party to save her own sanity – but the tea party went on unchanged without her. Perhaps if Alice joined up with Bob and stayed a little longer, the cycle might be broken.
There is lots of attention and activity around the question of how government should be made to work better, and in particular how it should be made to work better with modern technology. There is much less attention given to the question of why doing that is a good thing. This piece is an attempt to fill that gap from somebody who has been thinking about these issues pretty much from the beginning. It’s an extremely good first answer, but it is, of course, not the only one possible. It will be interesting to see if others rise to the challenge Tom poses.
Tom Steinberg – Civic Hall
This is a good antidote to the kind of technological determinism which is a frequent substitute for strategic thinking. It recognises instead the importance of social and economic consequences of new technologies and, crucially, that they take a long time to play out. It is slightly weaker on introducing a new emphasis on data to the discussion, rightly recognising that traditional legal and regulatory models don’t easily fit the dispersed complexity of data, but perhaps missing the thought that data is already showing strong signs of following the stages towards organisational capture described by Tim Wu in one of the books which are the foundation for this article.
Peter Wells – Medium
This is a powerful challenge to everybody who works in any part of government. The system is fundamentally broken because its components were never designed or intended to act coherently and effectively as a system – and they don’t. There is considerable power in that diagnosis, implying if not quite drawing the conclusion that if you want to change the system, you have to change the system. The problem, of course, is that that is both intrinsically very hard to do and never seems to be as important or urgent as addressing specific policy problems – which is where we came in. So the hard question is not whether a better system could be devised (because there can be little doubt that it could be); it is whether the current system has the capability to make the changes needed. It is hard to stop and start again from scratch. That’s not just true of the UK – it has been argued that the most needed amendment to the US constitution is to make it easier to make constitutional amendments, which is probably impossible. None of that makes Straw’s diagnosis wrong, but it does underline that the route to change is as critical as the destination.
This is highly distilled very uncommon sense from a sharp observer of government in both the UK and Australia. At one level it’s about user research, which is unsurprising as that’s the core of what Leisa does, but the implications are very much wider – which is equally unsurprising as that is the ever less hidden power of user research.
As she observes, most people want to spend as little time as possible thinking about government services. Much follows from that simple insight.
Leisa Reichelt – disambiguity
Great practical advice on how to join policy and digital thinking together, applying the principle of going to where the user is – in this case the policy expert. Tracey writes with the empathy which comes from understanding both communities, and rightly reminds her digital audience of the merits of policy people – but also perpetuates two ideas, one tacit and one explicit, which risk hampering the ambition. The first is that policy people have much to learn from digital but digital people have little to learn from policy. The second is that the goal is for policy and digital to be recognised as being the same thing. Perhaps instead we can aim for inclusion while celebrating diversity.
Tracey Williams Allred – Medium
Technology is rarely just about technology, a fact often overlooked in slightly hysterical predictions about the impact of AI on jobs. This is a good summary of social, political and financial reasons why the path to universal automation might not be as straightforward as it is often portrayed. And that is to say nothing of the reasons to think that the technology itself may have intrinsic limitations as a substitute for humans.
Fabian Wallace-Stephens – the RSA
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.”
There is a caricature of policy making in which it is presented as an exercise in introspection, free of evidence and free in particular of contact with those who might experience and understand the context and impact of its delivery. Like all good caricatures, there is something recognisable in that, and like all good caricatures, those caricatured more easily see the distortion than the likeness.
The underlying challenge in this post is a good one. Emphasis on things which can be measured distorts attention from things which may be just as important but are more elusive. Understanding the variation around a central figure is as important as understanding the central figure itself – and can tell you very different things. Broader and more qualitative approaches are an essential complement to narrower and more quantitative ones.
But policy makers are people too. Dismissing them as ivory towered elitists is too easy. It would be good to have more empathetic policy makers, but more empathy with policy makers is part of what we need to get there. Policy making is itself the product of a system – and understanding the drivers and behaviours of that system is the essential first step to changing it.
Emmanuel Lee – LSE Impact Blog
This is an extract from a new book, The Mathematical Corporation: Where Machine Intelligence and Human Ingenuity Achieve the Impossible (out last month as an ebook, but not available on paper until September). The focus in the extract focuses on the ethics of data, with a simple explanation of differential privacy and some equally simply philosophical starting points for thinking about ethical questions.
There is nothing very remarkable in this extract, but perhaps worth a look for two reasons. The first is that the book from which it comes has a lot of promise; the second is a trenchant call to arms in its final line: ethical reasoning is about improving strategic decision making.
Josh Sullivan and Angela Zutavern – Sloan Review
A neat example of lateral thinking (and of the power of data) in government service design – can we make services more efficient by spreading demand and so lowering the expense of peak capacity?
As the post itself acknowledges, that won’t magic away the demand pressures on public services, but it does illustrate the value of what we might call second level optimisation – one above trying to maximise efficiency of the current way of doing things, and one below a more radical reconceptualisation of the service as a whole. All three are necessary, but the one in the middle is perhaps the one most easily overlooked.
Eddie Copeland – Nesta