Artificial Intelligence and Human Decision-making.

Recent developments in artificial intelligence are allowing an increasing number of decisions to be passed from human to machine. Most of these to date are operational decisions – such as algorithms on the financial markets deciding what trades to make and how. However, the range of such decisions that can be computerisable are increasing, and as many operational decisions have moral consequences, they could be considered to have a moral component.

One area in which this is causing growing concern is military robotics. The degree of autonomy with which uninhabited aerial vehicles and ground robots are capable of functioning is steadily increasing. There is extensive debate over the circumstances in which robotic systems should be able to operate with a human “in the loop” or “on the loop” – and the circumstances in which a robotic system should be able to operate independently. A coalition of international NGOs recently launched a campaign to “stop killer robots”.

While there have been strong arguments raised against robotic systems being able to use lethal force against human combatants autonomously, it is becoming increasingly clear that in many circumstances in the near future the “no human in the loop” robotic system will have advantages over the “in the loop system”. Automated systems already have better perception and faster reflexes than humans in many ways, and are slowed down by the human input. The human “added value” comes from our judgement and decision-making – but these are by no means infallible, and will not always be superior to the machine’s. In June’s Centre for a New American Society (CNAS) conference, Rosa Brooks (former pentagon official, now Georgetown Law professor) put this in a provocative way:
“Our record- we’re horrible at it [making “who should live and who should die” decisions] … it seems to me that it could very easily turn out to be the case that computers are much better than we are doing. And the real ethical question would be can we ethically and lawfully not let the autonomous machines do that when they’re going to do it better than we will.” (1)

For a non-military example, consider the adaptation of IBM’s Jeopardy-winning “Watson” for use in medicine. As evidenced by IBM’s technical release this week, progress in developing these systems continues apace (shameless plug: Selmer Bringsjord, the AI researcher “putting Watson through college” will speak in Oxford about “Watson 2.0″ next month as part of the Philosophy and Theory of AI conference).

Soon we will have systems that will enter use as doctor’s aides – able to analyse the world’s medical literature to diagnose a medical problem and provide recommendations to the doctor. But it seems likely that a time will come when these thorough analyses produce recommendations that are sometimes at odds with the doctor’s recommendation – but are proven to be more accurate on average. To return to combat, we will have robotic systems that can devise and implement non-intuitive (to human) strategies that involve using lethal force, but achieve a military objective more efficiently with less loss of life. Human judgement added to the loop may prove to be an impairment.

Moral Outsourcing

At a recent academic workshop I attended on autonomy in military robotics, a speaker posed a pair of questions to test intuitions on this topic.
“Would you allow another person to make a moral decision on your behalf? If not, why not?” He asked the same pair of questions substituting “machine” for “a person”.

Regarding the first pair of questions, we all do this kind of moral outsourcing to a certain extent – allowing our peers, writers, and public figures to influence us. However, I was surprised to find I was unusual in doing this in a deliberate and systematic manner. In the same way that I rely on someone with the right skills and tools to fix my car, I deliberately outsource a wide range of moral questions to people who I know can answer then better than I can. These people tend to be better-informed on specific issues than I am, have had more time to think them through, and in some cases are just plain better at making moral assessments. I of course select for people who have a roughly similar world view to me, and from time to time do “spot tests” – digging through their reasoning to make sure I agree with it.

We each live at the centre of a spiderweb of moral decisions – some obvious, some subtle. As a consequentialist I don’t believe that “opting out” by taking the default course or ignoring many of them absolves me of responsibility. However, I just don’t have time to research, think about, and make sound morally-informed decisions about my diet, the impact of my actions on the environment, feminism, politics, fair trade, social equality – the list goes on. So I turn to people who can, and who will make as good a decision as I would in ideal circumstances (or a better one) nine times out of ten.

So Why Shouldn’t I Trust The Machine?

So to the second pair of questions:
“Would you allow a machine to make a moral decision on your behalf? If not, why not?”

It’s plausible that in the near future we will have artificial intelligence that for given, limited situations (for example: make a medical treatment decision, a resource allocation decision, or an “acquire military target” decision) is able to weigh up the facts for a and make as a decision or better than a human can 99.99% of the time – unclouded by bias, with vastly more information available to it.

So why not trust the machine?

Human decision-making is riddled with biases and inconsistencies, and can be impacted heavily by as little as fatigue, or when we last ate. For all that, our inconsistencies are relatively predictable, and have bounds. Every bias we know about can be taken into account, and corrected for to some extent. And there are limits to how insane an intelligent, balanced person’s “wrong” decision will be – even if my moral “outsourcees” are “less right” than me 1 time out of 10, there’s a limit to how bad their wrong decision will be.

This is not necessarily the case with machines. When a machine is “wrong”, it can be wrong in a far more dramatic way, with more unpredictable outcomes, than a human could.

Simple algorithms should be extremely predictable, but can make bizarre decisions in “unusual” circumstances. Consider the two simple pricing algorithms that got in a pricing war, pushing the price of a book about flies to $23 million. Or the 2010 stock market flash crash. It gets even more difficult to keep track of when evolutionary algorithms and other “learning” methods are used. Using self-modifying heuristics Douglas Lenat’s Eurisko won the US Championship of the Traveller TCS game using unorthodox, non-intuitive fleet designs. This fun youtube video shows a Super Mario-playing greedy algorithm figuring out how to make use of several hitherto-unknown game glitches to win (see 10:47).

Why should this concern us? As the decision-making processes become more complicated, and the strategies more non-intuitive, it becomes ever-harder to “spot test” if we agree with them – provided the results turn out good the vast majority of the time. The upshot is that we have to just “trust” the methods and strategies more and more. It also becomes harder to figure out how, why, and in what circumstances the machine will go wrong – and what the magnitude of the failure will be.

Even if we are outperformed 99.99% of the time, the unpredictability of the 0.01% failures may be a good reason to consider carefully what and how we morally outsource to the machine.

1. Transcript available here.