pickerSocial problems of AI are raised in two government reports issued recently. The first is Preparing for the Future of Artificial Intelligence, from the Executive Office of the President of the USA; the second is Robotics and Artificial Intelligence, from the Science and Technology Committee of the UK House of Commons. The two reports cover similar ground, both aim for a comprehensive overview, and they share a generally level-headed and realistic tone. Neither of them choose to engage with the wacky prospect of the Singularity, for example, beyond noting that the discussion exists, and you will not find any recommendations about avoiding the attention of the Basilisk (though I suppose you wouldn’t if they believed in it, would you?). One exception to the  ‘sensible’ outlook of the reports is McKinsey’s excitable claim, cited in the UK report, that AI is having a transformational impact on society three thousand times that of the Industrial Revolution. I’m not sure I even understand what that means, and I suspect that Professor Tony Prescott from the University of Sheffield is closer to the truth when he says that:

“impacts can be expected to occur over several decades, allowing time to adapt”

Neither report seeks any major change in direction though they make detailed recommendations for nudging various projects onward. The cynical view might be that like a lot of government activity, this is less about finding the right way forward and more about building justification. Now no-one can argue that the White House or Parliament has ignored AI and its implications. Unfortunately the things we most need to know about – the important risks and opportunities that haven’t been spotted – are the very things least likely to be identified by compiling a sensible summary of the prevailing consensus.

Really, though, these are not bad efforts by the prevailing standards. Both reports note suggestions that additional investment could generate big economic rewards. The Parliamentary report doesn’t press this much, choosing instead to chide the government for not showing more energy and engagement in dealing with the bodies it has already created. The White House report seems more optimistic about the possibility of substantial government money, suggesting that a tripling of federal investment in basic research could be readily absorbed. Here again the problem is spotting the opportunities. Fifty thousand dollars invested in some robotics business based in a garden shed might well be more transformative than fifty million to enhance one of Google’s projects, but the politicians and public servants making the spending decisions don’t understand AI well enough to tell, and their generally large and well-established advisers from industry and universities are bound to feel that they could readily absorb the extra money themselves. I don’t know what the answer is here (if I had a way of picking big winners I’d probably be wealthy already), but for the UK government I reckon some funding for intelligent fruit and veg harvesters might be timely, to replace the EU migrant workers we might not be getting any more.

What about those social issues? There’s an underlying problem we’ve touched on before, namely that when AIs learn how to do a job themselves we often cannot tell how they are doing it. This may mean that they are using factors that work well with their training data but fail badly elsewhere or are egregiously inappropriate. One of the worst cases, noted in both reports, is Google’s photos app, which was found to tag black people as “gorillas” (the American report describes this horrific blunder without mentioning Google at all, though it presents some excuses and stresses that the results were contrary to the developers’ values – almost as if Google edited the report). Microsoft has had its moments, too, of course, notably with its chatbot Tay, that was rapidly turned into a Hitler-loving hate speech factory (This was possible because modern chatbots tend to harvest responses from the ones supplied by human interlocutors; in this case the humans mischievously supplied streams of appalling content. Besides exposing the shallowness of such chatbots, this possibly tells us something about human beings, or at least about the ones who spend a lot of time on the internet.)

Cases such as these are offensive, but far more serious is the evidence that systems used to inform decisions on matters such as probation or sentencing incorporate systematic racial bias. In all these instances it is of course not the case that digital systems are somehow inherently prone to prejudice; the problem is usually that they are being fed with data which is already biased. Google’s picture algorithm was presumably given a database of overwhelmingly white faces; the sentencing records used to develop the software already incorporated unrecognised bias. AI has always forced us to make explicit some of the assumptions we didn’t know we were making; in these cases it seems the mirror is showing us something ugly. It can hardly help that the industry itself is rather lacking in diversity: the White House report notes the jaw-dropping fact that the highest proportion of women among computer science graduates was recorded in 1984: it was 37% then and has now fallen to a puny 18%. The White House cites an interesting argument from Moritz Hardt intended to show that bias can emerge naturally without unrepresentative data or any malevolent  intent: a system looking for false names might learn that fake ones tended to be unusual and go on to pick out examples that merely happened to be unique in its dataset. The weakest part of this is surely the assumption that fake names are likely to be fanciful or strange – I’d have thought that if you were trying to escape attention you’d go generic? But perhaps we can imagine that low frequency names might not have enough recorded data connected with them to secure some kind of positive clearance and so come in for special attention, or something like that. But even if that kind of argument works I doubt that is the real reason for the actual problems we’ve seen to date.

These risks are worsened because they may occur in subtle forms that are difficult to recognise, and because the use of a computer system often confers spurious authority on results. The same problems may occur with medical software. A recent report in Nature described how systems designed to assess the risk of pneumonia rated asthmatics as zero risk; this was because their high risk led to them being diverted directly to special care and therefore not appearing in the database as ever needing further first-line attention. This absolute inversion of the correct treatment was bound to be noticed, but how confident can we be that more subtle mistakes would be corrected? In the criminal justice system we could take a brute force approach by simply eliminating ethnic data from consideration altogether; but in medicine it may be legitimately relevant, and in fact one danger is that risks are assessed on the basis of a standard white population, while being significantly different for other ethnicities.

Both reports are worthy, but I think they sometimes fall into the trap of taking the industry’s aspirations or even its marketing, as fact. Self-driving cars, we’re told, are likely to improve safety and reduce accidents. Well, maybe one day: but if it were all about safety and AIs were safer, we’d be building systems that left the routine stuff to humans and intervened with an over-ride when the human driver tried to do something dangerous. In fact it’s the other way round; when things get tough the human is expected to take over. Self-driving cars weren’t invented to make us safe, they were invented to relieve us of boredom (like so much of our technology, and indeed our civilisation). Encouraging human drivers to stop paying attention isn’t likely to be an optimal safety strategy as things stand.

I don’t think these reports are going to hit either the brakes or the accelerator in any significant way: AI, like an unsupervised self-driving car, is going to keep on going wherever it was going anyway.


  1. 1. Michael Murden says:

    If I had to guess, I’d guess that self-driving cars and most other attempts to automate routine tasks are more about cutting labor costs than anything else.

    I think the best way to deal with Newcomb’s paradox is to flip a coin.

    My sense from reading this blog, Rants Within the Undead God, Three Pound Brain and others is that ever since Nietzsche declared God dead atheists have been trying to resurrect him in different, usually mechanical guises. I’m fine with that.

  2. 2. Michael Murden says:

    Regarding racist robots, none of us, human or machine, is any better than our training. If there is one reason to worry about AI it’s the fact that AI funding comes mainly from governments and corporations, and therefore is likely to be no more benevolent than those entities.

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