10 questions to answer before using AI in public sector algorithmic decision making

Eddie Copeland – NESTA

A few months ago, Eddie Copeland shared 10 Principles for Public Sector use of Algorithmic Decision Making. They later apparently morphed into twenty questions to address, and now the twenty have been slimmed down to ten. They are all  good questions, but one very important one seems to be missing – how can decisions based on the algorithm be challenged? (and what, therefore, do people affected by a decision need to understand about how it was reached?)

 

Why Technology Favors Tyranny

Yuval Noah Harari – The Atlantic

The really interesting effects of technology are often the second and third order ones. The invention of electricity changed the design of factories. The invention of the internal combustion engine changed the design of cities. The invention of social media shows signs of changing the design of democracy.

This essay is a broader and bolder exploration of the consequences of today’s new technologies. That AI will destroy jobs is a common argument, that it might destroy human judgement and ability to make decisions is a rather bolder one (apparently a really creative human chess move is now seen as an indicator of potential cheating, since creativity in chess is now overwhelmingly the province of computers).

The most intriguing argument is that new technologies destroy the comparative advantage of democracy over dictatorship. The important difference between the two, it asserts, is not between their ethics but between their data processing models. Centralised data and decision making used to be a weakness; increasingly it is a strength.

There is much to debate in all that, of course. But the underlying point, that those later order effects are important to recognise, understand and address, is powerfully made.

Identifiers and data sharing

Leigh Dodds

This post – which is actually a set of tweets gathered together – is a beautifully short and simple explanation of why some basic stuff really matters in efficiently integrating data and the services it supports (and is actually quite important as well in ensuring that things don’t get joined up which shouldn’t be). Without common identifiers, simple and value adding connections get difficult, expensive and unreliable – a point powerfully made in a post linked from that one which sets out a bewildering array of unique identifiers for property in the UK – definitely unique in the sense that there is a one to one mapping between identifier and place, but ludicrously far from unique in their proliferation.

There is a huge appetite for making more effective use of data. The appetite needs to be as strong for creating the conditions which make that possible.

Data as property

Jeni Tennison – Exponential View

Azeem Azhar’s Exponential View is one of the very few weekly emails which earns regular attention, and it is no disrespect to him to say that the occasional guest authors he invites add further to the attraction. This edition is by Jeni Tennison, bringing her very particular eye to the question of data ownership.

Is owning data just like owning anything else? The simple answer to that is ‘no’. But if it isn’t, what does it mean to talk about data as property? To which the only simple answer is that there is no simple answer. This is not the place to look for detailed exposition and analysis, but it is very much the place to look for a set of links to a huge range of rich content, curated by somebody who is herself a real expert in the field.

Fading out the Echo of Consumer Protection: An empirical study at the intersection of data protection and trade secrets

Guido Noto La Diega

This is by way of a footnote to the previous post – a bit more detail on one small part of the enormous ecosystem described there.

If you buy an Amazon Echo then, partly depending on what you intend to do with it, you may be required to accept 17 different contracts, amounting to close to 50,000 words, not very far short of the length of a novel. You will also be deemed to be monitoring them all for any changes, and to have accepted any such changes by default.

That may be extreme in length and complexity, but the basic approach has become normal to the point of invisibility. That raises a question about the reasonableness of Amazon’s approach. But it raises a much more important question about our wider approach to merging new technologies into existing social, cultural and legal constructs. This suggests, to put it mildly, that there is room for improvement.

(note that the link is to a conference agenda page rather than directly to the presentation, as that is a 100Mb download, but if needed this is the direct link)

Anatomy of an AI System

Kate Crawford and Vladan Joler

An Amazon Echo is a simple device. You ask it do things, and it does them. Or at least it does something which quite a lot of the time bears some relation to the thing you ask it do. But of course in order to be that simple, it has to be massively complicated. This essay, accompanied by an amazing diagram (or perhaps better to say this diagram, accompanied by an explanatory essay), is hard to describe and impossible to summarise. It’s a map of the context and antecedents which make the Echo possible, covering everything from rare earth geology to the ethics of gathering training data.

It’s a story told in a way which underlines how much seemingly inexorable technology in fact depends on social choices and assumptions, where invisibility should not be confused with inevitability. In some important ways, though, invisibility is central to the business model – one aspect of which is illustrated in the next post.

MIT taught a neural network how to show its work

Tristan Greene – The Next Web

One of the many concerns about automated decision making is its lack of transparency. Particularly (but by no means only) for government services, accountability requires not just that decisions are well based, but that they can be challenged and explained. AI black boxes may be efficient and accurate, but they are not accountable or transparent.

This is an interesting early indicator that those issues might be reconciled. It’s in the special – and much researched – area of image recognition, so a long way from a general solution, but it’s encouraging to see systematic thought being addressed to the problem.

Table of Disruptive Technologies

Richard Watson and Anna Cupani – Imperial Tech Foresight

table of disruptove technologiesHere are a hundred disruptive technologies, set out in waves of innovation, with time to ubiquity on one axis and potential for disruption on the other. On that basis, smart nappies appear in the bottom left corner, as imminent and not particularly disruptive (though perhaps that depends on just how smart they are and on who is being disrupted), while towards the other end of the diagonal we get to transhuman technologies – and then who knows what beyond.

The authors are firm that this is scientific foresight, not idle futurism, though that’s an assertion which doesn’t always stand up to close scrutiny. Planetary colonisation is further into the future than implantable phones, but will apparently be less disruptive when it comes. Dream recording falls in to the distant future category (rather than fringe science, where it might appear more at home), rather oddly on the same time scale but three levels of disruption higher than fusion power.

The table itself demonstrates that dreams are powerful. But perhaps not quite that powerful. And it’s a useful reminder, yet again, that technology change is only ever partly about the technology, and is always about a host of other things as well.

The Fast-Follower Strategy for Technology in Government

David Eaves and Ben McGuire – Governing

Governments should move slowly and try not to break things. That’s a suggestion slightly contrary to the fashionable wisdom in some quarters, but has some solid reasoning behind it. There are good reasons for governments not to be leading edge adopters – government services should work; innovation is not normally a necessary counter to existential threats; service users are not able to trade stability for excitement.

That’s not an argument against innovation, but it is an argument for setting pace and risk appropriately. As a result, this post argues, the skills government needs are less to do cutting edge novelty, and much more to do with identifying and adopting innovations from elsewhere.

Self-Driving Cars Are Not the Future

Paris Marx – Medium

If you fall into the trap of thinking that technology-driven change is about the technology, you risk missing something important. No new technology arrives in a pristine environment, there are always complex interactions with the existing social, political, cultural, economic, environmental and no doubt other contexts. This post is a polemic challenging the inevitability – and practicality – of self-driving cars, drawing very much on that perspective.

The result is something which is interesting and entertaining in its own right, but which also makes a wider point. Just as it’s not technology that’s disrupting our jobs, it’s not technology which determines how self-driving cars disrupt our travel patterns and land use. And over and over again, the hard bit of predicting the future is not the technology but the sociology,

Too Many Projects

Rose Hollister and Michael Watkins – Harvard Business Review

The hardest bit of strategy is not thinking up the goal and direction in the first place. It’s not even identifying the set of activities which will move things in the desired direction. The hardest bit is stopping all the things which aren’t heading in that direction or are a distraction of attention or energy from the most valuable activities. Stopping things is hard. Stopping things which aren’t necessarily failing to do the thing they were set up to do, but are nevertheless not the most important things to be doing, is harder. In principle, it’s easier to stop things before they have started than to rein them in once they have got going, but even that is pretty hard.

In all of that, ‘hard’ doesn’t mean hard in principle: the need, and often the intention, is clear enough. It means instead that observation of organisations, and particularly larger and older organisation, provides strong reason to think that it’s hard in practice. Finding ways of doing it better is important for many organisations.

This article clearly and systematically sets out what underlies the problem, what doesn’t work in trying to solve it – and offers some very practical suggestions for what does. Practical does not, of course, mean easy. But if we don’t start somewhere, project sclerosis will only get worse.