Wednesday, March 08, 2017

Does AI pose a threat to society?

Last week I had the pleasure of debating the question "does AI pose a threat to society?" with friends and colleagues Christian List, Maja Pantic and Samantha Payne. The event was organised by the British Academy and brilliantly chaired by the Royal Society's director of science policy Claire Craig.

Here is my opening statement:

One Friday afternoon in 2009 I was called by a science journalist at, I recall, the Sunday Times. He asked me if I knew that there was to be a meeting of the AAAI to discuss robot ethics. I said no I don’t know of this meeting. He then asked “are you surprised they are meeting to discuss robot ethics” and my answer was no. We talked some more and agreed it was actually a rather dull story: a case of scientists behaving responsibly. I really didn’t expect the story to appear but checked the Sunday paper anyway, and there in the science section was the headline Scientists fear revolt of killer robots. (I then spent the next couple of days on the radio explaining that no, scientists do not fear a revolt of killer robots.)

So, fears of future super intelligence - robots taking over the world - are greatly exaggerated: the threat of an out-of-control super intelligence is a fantasy - interesting for a pub conversation perhaps. It’s true we should be careful and innovate responsibly, but that’s equally true for any new area of science and technology. The benefits of robotics and AI are so significant, the potential so great, that we should be optimistic rather than fearful. Of course robots and intelligent systems must be engineered to very high standards of safety for exactly the same reasons that we need our washing machines, cars and airplanes to be safe. If robots are not safe people will not trust them. To reach it’s full potential what robotics and AI needs is a dose of good old fashioned (and rather dull) safety engineering.

In 2011 I was invited to join a British Standards Institute working group on robot ethics, which drafted a new standard BS 8611 Guide to the ethical design of robots and robotic systems, published in April 2016. I believe this to be the world’s first standard on ethical robots.

Also in 2016 the very well regarded IEEE standards association – the same organization that gave us WiFi - launched a Global initiative on Ethical Considerations in AI and Autonomous Systems. The purpose of this Initiative is to ensure every technologist is educated and empowered to prioritize ethical considerations in the design and development of autonomous and intelligent systems; in a nutshell, to ensure ethics are baked in. In December we published Ethically Aligned Design: A Vision for Prioritizing Human Well Being with AI and Autonomous Systems. Within that initiative I'm also leading a new standard on transparency in autonomous systems, based on the simple principle that it should always be possible to find out why an AI or robot made a particular decision.

We need to agree ethical principles, because they are needed to underpin standards – ways of assessing and mitigating the ethical risks of robotics and AI. But standards needs teeth and in turn underpin regulation. Why do we need regulation? Think of passenger airplanes; the reason we trust them is because it's a highly regulated industry with an amazing safety record, and robust, transparent processes of air accident investigation when things do go wrong. Take one example of a robot that we read a lot about in the news – the Driverless Car. I think there's a strong case for a driverless car equivalent of the CAA, with a driverless car accident investigation branch. Without this it's hard to see how driverless car technology will win public trust.

Does AI pose a threat to society? No. But we do need to worry about the down to earth questions of present day rather unintelligent AIs; the ones that are deciding our loan applications, piloting our driverless cars or controlling our central heating. Are those AIs respecting our rights, freedoms and privacy? Are they safe? When AIs make bad decisions, can we find out why? And I worry too about the wider societal and economic impacts of AI. I worry about jobs of course, but actually I think there is a bigger question: how can we ensure that the wealth created by robotics and AI is shared by all in society?

Thank you.

This image was used to advertise the BA's series of events on the theme Robotics, AI and Society. The reason I reproduce it here is that one of the many interesting questions to the panel was about the way that AI tends to be visualised in the media. This kind of human face coalescing (or perhaps emerging) from the atomic parts of the AI seems to have become a trope for AI. Is it a helpful visualisation of the human face of AI, or does it mislead to an impression that AI has human characteristics?

Wednesday, February 15, 2017

Thoughts on the EU's draft report on robotics

A few weeks ago I was asked to write a short op-ed on the European Parliament Law Committee's recommendations on civil law rules for robotics.

In the end the piece didn't get published, so I am posting it here.

It is a great shame that most reports of the European Parliament’s Committee for Legal Affairs’ vote last week on its Draft Report on Civil Law Rules on Robotics headlined on ‘personhood’ for robots, because the report has much else to commend it. Most important among its several recommendations is a proposed code of ethical conduct for roboticists, which explicitly asks designers to research and innovate responsibly. Some may wonder why such an invitation even needs to be made but, given that engineering and computer science education rarely includes classes on ethics (it should), it is really important that robotics engineers reflect on their ethical responsibilities to society – especially given how disruptive robot technologies are. This is not new – great frameworks for responsible research and innovation already exist. One such is the 2014 Rome Declaration on RRI, and in 2015 the Foundation for Responsible Robotics was launched.

Within the report’s draft Code of Conduct is a call for robotics funding proposals to include a risk assessment. This too is a very good idea and guidance already exists in British Standard BS 8611, published in April 2016. BS 8611 sets out a comprehensive set of ethical risks and offers guidance on how to mitigate them. It is very good also to see that the Code stresses that humans, not robots, are the responsible agents; this is something we regarded as fundamental when we drafted the Principles of Robotics in 2010.

For me transparency (or the lack of it) is an increasing worry in both robots and AI systems. Labour’s industry spokesperson Chi Onwurah is right to say, “Algorithms are part of our world, so they are subject to regulation, but because they are not transparent, it’s difficult to regulate them effectively” (and don’t forget that it is algorithms that make intelligent robots intelligent). So it is very good to see the draft Code call for robotics engineers to “guarantee transparency … and right of access to information by all stakeholders”, and then in the draft ‘Licence for Designers’: you should ensure “maximal transparency” and even more welcome “you should develop tracing tools that … facilitate accounting and explanation of robotic behaviour… for experts, operators and users”.  Within the IEEE Standards Association Global Initiative on Ethics in AI and Autonomous Systems, launched in 2016, we are working on a new standard on Transparency in Autonomous Systems.

This brings me to standards and regulation.  I am absolutely convinced that regulation, together with transparency and public engagement, builds public trust. Why is it that we trust our tech? Not just because it’s cool and convenient, but also because it’s safe (and we assume that the disgracefully maligned experts will take care of assuring that safety). One of the reasons we trust airliners is that we know they are part of a highly regulated industry with an amazing safety record. The reason commercial aircraft are so safe is not just good design, it is also the tough safety certification processes and, when things do go wrong, robust processes of air accident investigation. So the Report’s call for a European Agency for Robotics and AI to recommend standards and regulatory framework is, as far as I’m concerned, not a moment too soon. We urgently need standards for safety certification of a wide range of robots, from drones and driverless cars to robots for care and assisted living.

Like many of my robotics colleagues I am deeply worried by the potential for robotics and AI to increase levels of economic inequality in the world. Winnie Byanyima, executive director of Oxfam writes for the WEF, “We need fundamental change to our economic model. Governments must stop hiding behind ideas of market forces and technological change. They … need to steer the direction of technological development”. I think she is right – we need a serious public conversation about technological unemployment and how we ensure that the wealth created by AI and Automonous Systems is shared by all. A Universal Basic Income may or may not be the best way to do this – but it is very encouraging to see this question raised in the draft Report.

I cannot close the piece without at least mentioning artificial personhood. My own view is that personhood is the solution to a problem that doesn’t exist. I can understand why, in the context of liability, the Report raises this question for discussion, but – as the report itself later asserts in the Code of Conduct: humans, not robots are the responsible agents. Robots are, and should remain, artefacts.

Friday, January 06, 2017

The infrastructure of life 2 - Transparency

Part 2: Autonomous Systems and Transparency

In my previous post I argued that a wide range of AI and Autonomous Systems (from now on I will just use the term AS as shorthand for both) should be regarded as Safety Critical. I include both autonomous software AI systems and hard (embodied) AIs such as robots, drones and driverless cars. Many will be surprised that I include in the soft AI category apparently harmless systems such as search engines. Of course no-one is seriously inconvenienced when Amazon makes a silly book recommendation, but consider very large groups of people. If a truth such as global warming is - because of accidental or willful manipulation - presented as false, and that falsehood believed by a very large number of people, then serious harm to the planet (and we humans who depend on it) could surely result.

I argued that the tools barely exist to properly assure the safety of AS, let alone the standards and regulation needed to build public trust, and that political pressure is needed to ensure our policymakers fully understand the public safety risks of unregulated AS.

In this post I will outline the case that transparency is a foundational requirement for building public trust in AS based on the radical proposition that it should always be possible to find out why an AS made a particular decision.

Transparency is not one thing. Clearly your elderly relative doesn't require the same level of understanding of her care robot as the engineer who repairs it. Not would you expect the same appreciation of the reasons a medical diagnosis AI recommends a particular course of treatment as your doctor. Broadly (and please understand this is a work in progress) I believe there are five distinct groups of stakeholders, and that AS must be transparent to each, in different ways and for different reasons. These stakeholders are: (1) users, (2) safety certification agencies, (3) accident investigators, (4) lawyers or expert witnesses and (5) wider society.
  1. For users, transparency is important because it builds trust in the system, by providing a simple way for the user to understand what the system is doing and why.
  2. For safety certification of an AS, transparency is important because it exposes the system's processes for independent certification against safety standards.
  3. If accidents occur, AS will need to be transparent to an accident investigator; the internal process that led to the accident need to be traceable. 
  4. Following an accident lawyers or other expert witnesses, who may be required to give evidence, require transparency to inform their evidence. And 
  5. for disruptive technologies, such as driverless cars, a certain level of transparency to wider society is needed in order to build public confidence in the technology.
Of course the way in which transparency is provided is likely to be very different for each group. If we take a care robot as an example transparency means the user can understand what the robot might do in different circumstances; if the robot should do anything unexpected she should be able to ask the robot 'why did you just do that?' and receive an intelligible reply. Safety certification agencies will need access to technical details of how the AS works, together with verified test results. Accident investigators will need access to data logs of exactly what happened prior to and during an accident, most likely provided by something akin to an aircraft flight data recorder (and it should be illegal to operate an AS without such a system). And wider society would need accessible documentary-type science communication to explain the AS and how it works.

In IEEE Standards Association project P7001, we aim to develop a standard that sets out measurable, testable levels of transparency in each of these categories (and perhaps new categories yet to be determined), so that Autonomous Systems can be objectively assessed and levels of compliance determined. It is our aim that P7001 will also articulate levels of transparency in a range that defines minimum levels up to the highest achievable standards of acceptance. The standard will provide designers of AS with a toolkit for self-assessing transparency, and recommendations for how to address shortcomings or transparency hazards.

Of course transparency on its own is not enough. Public trust in technology, as in government, requires both transparency and accountability. Transparency is needed so that we can understand who is responsible for the way Autonomous Systems work and - equally importantly - don't work.

Thanks: I'm very grateful to colleagues in the IEEE global initiative on ethical considerations in Autonomous Systems for supporting P7001, especially John Havens and Kay Firth-Butterfield. I'm equally grateful to colleagues at the Dagstuhl on Engineering Moral Machines, especially Michael Fisher, Marija Slavkovik and Christian List for discussions on transparency.

Related blog posts:
The Infrastructure of Life 1 - Safety
Ethically Aligned Design
How do we trust our Robots?
It's only a matter of time

Sunday, January 01, 2017

The infrastructure of life 1 - Safety

Part 1: Autonomous Systems and Safety

We all rely on machines. All aspects of modern life, from transport to energy, work to welfare, play to politics depend on a complex infrastructure of physical and virtual systems. How many of us understand how all of this stuff works? Very few I suspect. But it doesn't matter, does it? We trust the good men and women (the disgracefully maligned experts) who build, manage and maintain the infrastructure of life. If something goes wrong they will know why. And (we hope) make sure it doesn't happen again.

All well and good you might think. But the infrastructure of life is increasingly autonomous - many decisions are now made not by a human but by the systems themselves. When you search for a restaurant near you the recommendation isn't made by a human, but by an algorithm. Many financial decisions are not made by people but by algorithms; and I don't just mean city investments - it's possible that your loan application will be decided by an AI. Machine legal advice is already available; a trend that is likely to increase. And of course if you take a ride in a driverless car, it is algorithms that decide when the car turns, brakes and so on. I could go on.

These are not trivial decisions. They affect lives. The real world impacts are human and economic, even political (search engine results may well influence how someone votes). In engineering terms these systems are safety critical. Examples of safety critical systems that we all rely on from time to time include aircraft autopilots or train braking systems. But - and this may surprise you - the difficult engineering techniques used to prove the safety of such systems are not applied to search engines, automated trading systems, medical diagnosis AIs, assistive living robots, delivery drones, or (I'll wager) driverless car autopilots.

Why is this? Well, it's partly because the field of AI and autonomous systems is moving so fast. But I suspect it has much more to do with an incompatibility between the way we have traditionally designed safety critical systems, and the design of modern AI systems. There is I believe one key problem: learning. There is a very good reason that current safety critical systems (like aircraft autopilots) don't learn. Current safety assurance approaches assume that the system being certified will never change, but a system that learns does – by definition – change its behaviour, so any certification is rendered invalid after the system has learned.

And as if that were not bad enough, the particular method of learning which has caused such excitement - and rapid progress - in the last few years is based on Artificial Neural Networks (more often these days referred to as Deep Learning). A characteristic of ANNs is that after the ANN has been trained with datasets, any attempt to examine its internal structure in order to understand why and how the ANN makes a particular decision is impossible. The decision making process of an ANN is opaque. Alphago's moves were beautiful but puzzling. We call this the black box problem.

Does this mean we cannot assure the safety of learning autonomous/AI systems at all? No it doesn't. The problem of safety assurance of systems that learn is hard but not intractable, and is the subject of current research*. The black box problem may be intractable for ANNs, but could be avoided by using approaches to AI that do not use ANNs.

But - here's the rub. This involves slowing down the juggernaut of autonomous systems and AI development. It means taking a much more cautious and incremental approach, and it almost certainly involves regulation (that, for instance, makes it illegal to run a driverless car unless the car's autopilot has been certified as safe - and that would require standards that don't yet exist). Yet the commercial and political pressure is to be more permissive, not less; no country wants to be left behind in the race to cash in on these new technologies.

This is why work toward AI/Autonomous Systems standards is so vital, together with the political pressure to ensure our policymakers fully understand the public safety risks of unregulated AI.

In my next blog post I will describe one current standards initiative, towards introducing transparency in AI and Autonomous Systems based on the simple principle that it should always be possible to find out why an AI/AS system made a particular decision.

The next few years of swimming against the tide is going to be hard work. As Luke
Muehlhauser writes in his excellent essay on transparency in safety-critical systems "...there is often a tension between AI capability and AI transparency. Many of AI’s most powerful methods are also among its least transparent".

*some, but nowhere near enough. See for instance Verifiable Autonomy.

Related blog posts:
Ethically Aligned Design
How do we trust our Robots?
It's only a matter of time