Posts tagged ‘robotics’

The EU is not really giving robots human rights; but some of its proposals are cause for concern. James Vincent on the Verge provides a sensible commentary which corrects the rather alarming headlines generated by the European Parliament draft report issued recently. Actually there are several reasons to keep calm. It’s only a draft, it’s only a report, it’s only the Parliament. It is said that in the early days the Eurocrats had to quickly suppress an explanatory leaflet which described the European Economic Community as a bus. The Commission was the engine, the Council was the driver; and the Parliament… was a passenger. Things have moved on since those days, but there’s still some truth in that metaphor.

A second major reason to stay calm, according to Vincent, is that the report (quite short and readable, by the way, though rather bitty) doesn’t really propose to treat robots as human beings; it mainly addresses the question of liability for the acts of autonomous robots. The sort of personhood it considers is more like the legal personhood of corporations. That’s true, although the report does invite trouble by cursorily raising the question of whether robots can be natural persons. Some other parts read strangely to me, perhaps because the report is trying to cover a lot of ground very quickly. At one point it seems to say that Asimov’s laws of robotics should currently apply to the designers and creators of robots; I suspect the thinking behind that somewhat opaque idea (A designer must obey orders given it by human beings except where such orders would conflict with the First Law?) has not been fully fleshed out in the text.

What about liability? The problem here is that if a robot does damage to property, turns on its fellow robots (pictured) or harms a human being, the manufacturer and the operator might disclaim responsibility because it was the robot that made the decision, or at least, because the robot’s behaviour was not reasonably predictable (I think predictability is the key point: the report has a rather unsatisfactory stab at defining smart autonomous robots, but for present purposes we don’t need anything too philosophical – it’s enough if we can’t tell in advance exactly what the machine will do).

I don’t see a very strong analogy with corporate personhood. In that case the problem is the plethora of agents; it simply isn’t practical to sue everyone involved in a large corporate enterprise, or even assign responsibility. It’s far simpler if you have a single corporate entity that can be held liable (and can also hold the assets of the enterprise, which it may need to pay compensation, etc). In that context the new corporate legal person simplifies the position, whereas with robots, adding a machine person complicates matters. Moreover, in order for the robot’s liability to be useful you would have to allow it to hold property which it could use to fund any liabilities. I don’t think anyone is currently thinking that roombas should come with some kind of dowry.

Note, however, that corporate personhood has another aspect; besides providing an entity to hold assets and sue or be sued, it typically limits the liability of the parties. I am not a lawyer, but as I understand it, if several people launch a joint enterprise they are all liable for its obligations; if they create a corporation to run the enterprise, then liability is essentially limited to the assets held by the corporation. This might seem like a sneaky way of avoiding responsibility; would we want there to be a similar get-out for robots? Let’s come back to that.

It seems to me that the basic solution to the robot liability problem is not to introduce another person, but to apply strict liability, an existing legal concept which makes you responsible for your product even if you could not have foreseen the consequences of using it in a particular case. The report does acknowledge this principle. In practice it seems to me that liability would partly be governed by the contractual relationship between robot supplier and user: the supplier would specify what could be expected given correct use and reasonable parameters – if you used your robot in ways that were explicitly forbidden in that contract, then liability might pass to you.

Basically, though, that approach leaves responsibility with the robot’s builder or supplier, which seems to be in line with what the report mainly advocates. In fact (and this is where things begin to get  a bit questionable) the report advocates a scheme whereby all robots would be registered and the supplier would be obliged to take out insurance to cover potential liability. An analogy with car insurance is suggested.

I don’t think that’s right. Car insurance is a requirement mainly because in the absence of insurance, car owners might not be able to pay for the damage they do. Making third-party insurance obligatory means that the money will always be there. In general I think we can assume by contrast that robot corporations, one way or another, will usually have the means to pay for individual incidents, so an insurance scheme is redundant. It might only be relevant where the potential liability was outstandingly large.

The thing is, there’s another issue here. If we need to register our robots and pay large insurance premiums, that imposes a real burden and a significant number of robot projects will not go ahead. Suppose, hypothetically, we have robots that perform crucial work in nuclear reactors. The robots are not that costly, but the potential liabilities if anything goes wrong are huge. The net result might be that nobody can finance the construction of these robots even though their existence would be hugely beneficial; in principle, the lack of robots might even stop certain kinds of plant from ever being built.

So the insurance scheme looks like a worrying potential block on European robotics; but remember we also said that corporate responsibility allows some limitation of liability. That might seem like a cheat, but another way of looking at it is to see it as a solution to the same kind of problem; if investors had to acept unlimited personal liability, there are some kinds of valuable enterprise that would just never be viable. Limiting liability allows ventures that otherwise would have potential downsides too punishing for the individuals involved. Perhaps, then, there actually is an analogy here and we ought to think about allowing some limitation of liability in the case of autonomous robots? Otherwise some useful machines may never be economically viable?

Anyway, I’m not a lawyer, and I’m not an economist, but I see some danger that an EU regime based on this report with registration, possibly licensing, and mandatory insurance, could significantly inhibit European robotics.

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.

Picture: pyramid of wisdom. Robots.net reports an interesting plea (pdf download) for clarity by Emanuel Diamant at the the 3rd Israeli Conference on Robotics. Robotics, he says, has been derailed for the last fifty years by the lack of a clear definition of basic concepts: there are more than 130 definitions of data, and more than 75 definitions of intelligence.

I wouldn’t have thought serious robotics had been going for much more than fifty years (though of course there are automata and other precursors which go much further back), so that sounds pretty serious: but he’s clearly right that there is a bad problem, not just for robotics but for consciousness and cognitive science, and not just for data, information, knowledge, intelligence, understanding and so on, but for many other key concepts, notably including ‘consciousness’.

It could be that this has something to do with the clash of cultures in this highly interdisciplinary area.  Scientists are relatively well-disciplined about terminology, deferring to established norms, reaching consensus and even establishing taxonomical authorities. I don’t think this is because they are inherently self-effacing or obedient; I would guess instead that this culture arises from two factors: first, the presence of irrefutable empirical evidence establishes good habits of recognising unwelcome truth gracefully; second, a lot of modern scientific research tends to be a collaborative enterprise where a degree of consensus is essential to progress.

How very different things are in the lawless frontier territory of philosophy, where no conventions are universally accepted, and discrediting an opponent’s terminology is often easier and no less prestigious than tackling the arguments. Numerous popular tactics seem designed to throw the terminology into confusion.  A philosopher may often, for instance, grab some existing words  – ethics/morality, consciousness/awareness, information/data, or whatever – and use them to embody a particular distinction while blithely ignoring the fact that in another part of the forest another philosopher is using the same words for a completely different distinction. When irreconcilable differences come to light a popular move is ‘giving’ the disputed word away:”Alright, then, you can just have ‘free will’ and make it what you like: I’m going to talk about ‘x-free will’ instead in future. I’ll define ‘x-free will’ to my own satisfaction and when I’ve expounded my theory on that basis I’ll put in a little paragraph pointing out that ‘x-free will’ is the only kind worth worrying about, or the only kind everyone in the real world is actually talking about”.  These and other tactics lead to a position where in some areas it’s generally necessary to learn a new set of terms for every paper: to have others picking up your definitions and using them in their papers, as happens with Ned Block’s p- and a-consciousness, for example, is a rare and high honour.

It’s not that philosophers are quarrelsome and egotistical (though of course they are);  it’s more that the subject matter rarely provides any scope for pinning down an irrefutable position, and is best tackled by single brains operating alone (Churchlands notwithstanding).

Diamant is particularly exercised by problems over ‘data’ , ‘information’, ‘knowledge’, and ‘intelligence’.  Why can’t we sort these out? He correctly identifies a key problem: some of these terms properly involve semantics, and the others don’t (needless to say, it isn’t clearly agreed which words fall into which camp).  What he perhaps doesn’t realise clearly enough is that the essential nature of semantics is an extremely difficult problem which has so far proved unamenable to science.  We can recognise semantics quite readily, and we know well enough the sort of thing semantics does; but exactly how it does those things remains a cloudy matter, stuck in the philosophical badlands.

If my analysis is right, the only real hope of clarification would be if we could come up with some empirical research (perhaps neurological, perhaps not) which would allow us to define semantics (or x-semantics at any rate), in concrete terms that could somehow be demonstrated in a lab. That isn’t going to happen any time soon, or possibly ever.

Diamant wants to press on however, and inevitably by doing so in the absence of science he falls into philosophy: he offers us implicitly a theory of his own and – guess what? Another new way of using the terminology. The theory he puts forward is that semantics is a matter of convention between entities. Conventions are certainly important: the meaning of particular words or symbols is generally a matter of convention; but that doesn’t seem to capture the essence of the thing. If semantics were simply a matter of convention, then before God created Adam he could have had no semantics, and could not have gone around asking for light; on the other hand, if we wanted a robot to deal with semantics, all we’d need to do would be to agree a convention with it or perhaps let it in on the prevailing conventions. I don’t know how you’d do that with a robot which had no semantics to begin with, as it wouldn’t be able to understand what you were talking about.

There are, of course, many established philosophical attempts to clarify the intentional basis of semantics. In my personal view the best starting point is H.P. Grice’s theory of natural meaning (those black clouds mean rain); although I think it’s advantageous to use a slightly different terminology…