This is thinking at epic scale.
Consider the Milky Way crashing into Andromeda in about four billion years from now, taking another billion years to establish some form of stability.
Now consider the unstoppable force of technological (and social change) colliding with the established cultures and practices of government.
Now reflect on how good a metaphor the first is for the second. There’s probably less than four billions years to wait until we find out.
Rachel Coldicutt – Doteveryone
The subtitle of this post lays down a challenge:
Why a social scientist could be the most important person on your product team
Leaving aside the point that it might be an even better challenge if ‘philosopher’ were substituted for ‘social scientist’, this is an important issue. There is much talk (and much writing) about the need for ethics in data and software – though curiously rather less so in service design, where it is no less important.
But ethics is not some esoteric form of quality assurance added as a final overlay to activities otherwise devoid of any moral compass. It is perhaps better understood (in this context) as the encapsulation of a deep and pervasive view that technology should work for humanity, not the other way round.
What would computer science look like if it included the perspective of humanities and social sciences from the outset? And what if that perspective came not from some thinker in residence, but from people who brought a fusion of perspectives and understanding to problem solving?
And whatever the answers to those questions might be, there is a wider one still: where does that fusion not have a place? The Amalgamated Union of Philosophers, Sages, Luminaries, and Other Professional Thinking Persons may be due for a resurgence.
Karen Hao – MIT Technology Review
What is artificial intelligence? It’s a beguilingly simple question, but one which lacks a beguilingly simple answer. There’s more than one way to approach the question, of course – Chris Yiu provides mass exemplificaiton, for example (his list had 204 entries when first linked from here in January, but has now grown to 501). Terence Eden more whimsically dives down through the etymology, while Fabio Ciucci provides a pragmatic approach based on the underlying technology.
This short post takes a different approach again – diagnose whether what you are looking at is AI by means of a simple flowchart. It’s a nice idea, despite inviting some quibbling about some of the detail (“looking for patterns in massive amounts of data” doesn’t sound like a complete account of “reasoning” to me). And it’s probably going to need a bigger piece of paper soon.
Benedict Dellot and Fabian Wallace-Stephens – RSA
This is a refreshing post about the implications of work being displaced by machines, which isn’t about the work, the displacement, or the machines. Instead it puts forward a range of suggestions about what would need to be in place to make the consequences of that displacement socially and economically beneficial.
The ideas themselves are still fairly undeveloped at this stage – this is more a prospectus of issues to be explored than the substantive exploration – but even in embryonic form, they demonstate that a wider range of responses is possible than is often assumed. At first sight, some of the ideas look considerably more robust than others, but regardless of their specific merits, being imaginative about ways of dealing with the consequences of technology change must be a better strategy than trying to impede it.
Rich Denyer-Bewick – CitizensOnline
We are better connected than ever before, through a bewildering array of devices and networks. And loneliness is an acute problem, undermining wellbeing and health. This post both explores that paradox and focuses more directly on its implications for the design of public services.
There is an apparently happy alignment between the improvements to quality which come from putting services online and the consequential efficiency savings which accrue to hard pressed public sector delivery organisations. But the reduction in human interaction which follows is a fundamental and deliberate feature of the new service design. It surely can’t be right that an occasional conversation with a harried bureaucrat will stave off the adverse effects of loneliness – but it always worth remembering that making services more impersonal is always likely disproportionately to affect those who are most vulnerable and most in need of support.
Michael Graber – Innovation Excellence
This short post asks a question which falls to be answered all too often. The answer it gives is that failure comes from the misperception that the most important thing about digital transformation is that it is digital:
Digital transformations are actually transformations of mindset, business model, culture, and operations. These are people problems, in the main, not technology issues.
Richard Susskind – The British Academy Review
The debate about the scale of the impact of automation on employment rumbles on. Opinions vary enormously both on the numbers and types of jobs affected and on the more esoteric question of whether jobs or tasks are the more useful unit of measurement.
This short article neatly sidesteps that debate altogether. Its focus is on outcomes, the things we want to achieve. They will remain unchanged even as the means of achieving them changes radically. So the core question is not whether the way humans achieve the outcome can be replicated by robots and AI, but rather whether there is an alternative – and perhaps very different – way of achieving the same outcome in a way which is optimised for machines, not people.
Framing the question that way does two things. The first is that it brings some much needed clarity to a complex issue. The second is that all of us who have been congratulating ourselves on our irreplaceability need to start worrying much sooner than we might have thought.
Tom Loosemore – Public Digital
This is a deceptively simple list which describes ways of working in internet-era organisations. The GDS design principles are clearly among its antecedents, but this is a broader and deeper approach, setting out how to make things work – and work well – in organisations. It’s hard to argue with the thrust of the advice given here, and in any case it ends with an admonition to do sensible things in the right way rather than stick rigidly to the rules.
That doesn’t make the approach beyond criticism, both in detail and in approach, though it does have the happy consequence that challenge and consequent improvement are themselves part of the model being advocated. With that starting point, there are a couple of places where a further iteration could improve things further.
One is the instruction to treat data as infrastructure. The thought behind that is a good one: data matters, and it matters that it is managed well. Well ordered data is part of infrastructure at every level from the national (and international) downwards. But data is also part of the superstructure. Managing, processing, and creating value out of data are fundamental to the purpose and activities of organisations. Both aspects need to be understood and integrated.
A more subtle issue is that while it might be clear what counts as good internet-era ways of working, much of that work happens in organisations which are barely of the internet era at all. Precisely because it does challenge established approaches, established power structures and established infrastructure of every kind, the path to adoption is far from straightforward. Looked at in that light, this list is oddly impersonal: it is couched in the imperative, but without being clear who the orders are addressed to. There is a dimension of behavioural and organisational change which never quite makes it to the centre of the narrative, but which for organisations which are not native to the internet era is critically important.
None of that is a reason for not following the advice given here. But some of it might be part of the explanation of why it needs to be given in the first place.
Sandra Wachter – Oxford Internet Institute
The ethical and legal issues around even relatively straightforward objectively factual personal data are complicated enough. But they seem simple beside the further complexity brought in by inferences derived from that data. Inferences are not new, of course: human beings have been drawing inferences about each other long before they had the assistance of machines. But as in other area, big data makes a big difference.
Inferences are tricky for several reasons. The ownership of an inference is clearly something different from ownership of the information from which the inference is drawn (even supposing that it is meaningful to talk about ownership in this context at all). An inference is often a propensity, which can be wrong without being falsifiable – ‘people who do x tend to like y‘ may remain true even I do x and don’t like y. And all that gets even more tricky over time – ‘people who do x tend to become y in later life’ can’t even be denied or contradicted at the individual level.
This lecture explores those questions and more, examining them at the intersection of law, technology and ethics – and then asks what rights we, as individuals, should have about the inferences which are made about us.
The same arguments are also explored in a blog post written by Wachter with her collaborator Brent Mittelstadt and in very much more detail in an academic paper, also written with Mittelstadt.
Irina Bolychevsky – Medium
As a corollary to the comment here a few weeks back on Tim Berners-Lee’s ideas for shifting the power balance of the web away from data-exploiting conglomerates and back towards individuals, this post is a good clear-headed account of why his goal – however laudable – may be hard to achieve in practice.
What makes it striking and powerful is that it is not written from the perspective of somebody critical of the approach. On the contrary, it is by a long-standing advocate of redecentralising the internet, but who has a hard-headed appreciation of what would be involved. It is a good critique, for example addressing the need to recognise that data does not perfectly map to individuals (and therefore what data counts as mine is nowhere near as straightforward as might be thought) and that for many purposes the attributes of the data, including the authority with which it is asserted, can be as important at the data itself.
One response to that and other problems could be to give up on the ambition for change in this area, and leave control (and thus power) with the incumbents. Instead, the post takes the more radical approach of challenging current assumptions about data ownership and control at a deeper level, arguing that governments should be providing the common, open infrastructure which would allow very different models of data control to emerge and flourish.
Kate Tarling and Matti Keltanen – Medium
This post is a deep and thoughtful essay on why large organisations struggle to find a clear direction and to sustain high quality delivery. At one level the solution is disarmingly simple: define what success looks like, work out how well the organisation is configured to deliver that success, and change the configuration if necessary – but in the meantime, since reconfiguration is slow and hard, be systematic and practical at developing and working through change.
If it were that easy, of course, everybody would have done it by now and all large organisations would be operating in a state of near perfection. Simple observation tells us that that is not the case, and simple experience tells us that it is not at all easy to fix. This post avoids the common trap of suggesting a simple – often simplistic – single answer, but instead acknowledges the need to find ways of moving forward despite the aspects of the organisational environment which hold things back. Even more usefully, it sets out an approach for doing that in practice based on real (and no doubt painful) experience.
If there were a weakness in this approach, it would be in appearing to underestimate some of the behavioural challenges, partly because the post notes, but doesn’t really address, the different powers and perspectives which come from different positions. The options – and frustrations – of a chief executive or board member are very different from those elsewhere in the organisation who may feel some of the problems more viscerally but find it harder to identify points of leverage to change things. The argument that in the absence of structures aligned to outcomes and goals we should fall back to alignment around purpose is a strong one, but the challenge of even achieving the fallback shouldn’t be underestimated.
It’s a pretty safe bet though that anybody struggling to find ways of helping large organisations to become fully effective will find ideas and insights here which are well worth reflecting on.
Martin Stewart-Weeks – Public Purpose
Geoff Mulgan has written a book about the power of collective intelligence. Martin Stewart-Weeks has amplified and added to Geoff’s work by writing a review. And now this note may spread attention and engagement a little further.
That is a ridiculously trite introduction to a deeply serious book. Spreading, amplifying, challenging and engaging with ideas and the application of those ideas are all critically important, and it’s hard to imagine serious disagreement with the proposition that it’s the right thing to do. But the doing of it is hard, to put it mildly. More importantly, that’s only one side of the driving problem: how do unavoidably collective problems get genuinely collective solutions? And in the end, that question is itself just such a problem, demanding just such a solution. Collectively, we need to find it. It’s well worth reading the book, but this review is a pretty good substitute.