IT’S (NOT) THE END OF THE WORLD AS WE KNOW IT (AND I FEEL FINE)

Michael Kohn discusses the potential effects of an AI capable of making decisions in moral dilemmas on society with Mr Greg Artus and Cosmin Badea.

“But what if we set the machine to work and it came up with an idea that we disagreed with? Would we have to reconsider our own views?”

It was still heavily snowing when I opened a zoom room to discuss an issue of apocalyptic proportions (or not) with two of my best-loved philosophy teachers. Together, they have considered the question of what a moral machine would look like, and how we could go about creating one. It began with Cosmin’s PhD work on the ‘how’ of the matter, while Greg has been looking at the philosophy of rules in games. This is relevant to a computer as this would have to be how it interprets our algorithms. They propose that currently there is something holding this possible breakthrough back, known as the interpretation problem.

Computers, such as the successful AlphaGo machine, understand how to play very complicated games. These work by the machine being told the set of rules and trying multiple possibilities before choosing the optimal move. However, in these cases, the rules are deterministic. It is easy to evaluate progress e.g. how many pieces have been taken, so it is easy for the computer to carry out the task in the way we desired. When we scale this to a question of deciding on morals, we run up against the problem that a rule cannot contain the criteria for its own application. It must be interpreted in some form, which can generate interpretations that satisfy the rules, but are wildly different from what we wanted.

They told me of Bostrom’s ‘paperclip maximiser’ thought experiment. With the goal of maximising the number of paperclips it could decide to steal or con people. So, let’s say we tell it that it cannot do either of these things, it could decide to use all our resources to make paperclips. Because, as Wittgenstein argues, language can be infinitely generated, therefore there are infinitely many meanings so we cannot focus the interpretation to get the desired goal. Greg also questions whether a machine could understand what a ‘goal’ itself is. If our goal is to cross a field and there are obstacles in the way, we would have to move around them to get to the other side. However, a computer may just see the rules as being “avoid obstacles” without understanding why these rules are here - the actual goal we wanted it to accomplish in the first place. This is linked to the deep question of how a computer perceives time, which I’ll leave the reader to ponder.

However, there is a potential way to avoid this problem. Rather than stating the moral rules we wish the machine to incorporate, we somehow show them to it, which is how Wittgenstein (a favourite of Greg) interprets the meaning of language in the first place. To do this, we use the idea of morality as being driven by values. We want to demonstrate a good character through ideas such as honesty or care for others to a

computer and hope it mimics it to a degree. The more general this is stated, the less chance it has of falling prey to the interpretation problem described above, as the meaning is not tied to representation in a language (such as any programming language). In doing so, however, the goal becomes less well defined, and we are also left with the problem of how to demonstrate these things to a computer while using as little written language as possible.

“We want to demonstrate a good character through ideas such as honesty or care for others to a computer and hope it mimics it to a degree.”

The mainstream media has always feared this sort of technology - from HAL to Ultron. Every time AI seems to be in films it seems to be in an overwhelmingly negative environment. Greg thinks this is understandable; it stems from a feeling of a lack of control that has existed since the idea of Mary Shelley’s Frankenstein. We have the idea of our children learning from us and us controlling them as we teach them. Here we could be dealing with something much more powerful than ourselves. He suggests that a way to avoid this would be to limit the power such a machine has access to, but Cosmin thinks that we would not need such a thing in the first place. He argues that since the developments in technology would be gradual, our understanding of morality and how to regulate such machines would have enough time to keep control of the machines, and, well, ‘solve’ the interpretation problem.

But what if we set the machine to work and it came up with an idea that we disagreed with? Would we have to reconsider our own views? The problem, they argue, is that we would not be able to see the reasoning of the machine, and therefore would have to discuss it ourselves and form our own conclusions. So, they say, the machine could give us new problems to consider, but could not fundamentally change our morality.

To conclude a delightful hour of philosophising, I asked both what they would use such a moral machine for. Cosmin’s eyes lit up as he described wanting to know the answer to very deep moral questions. The machine could learn so much philosophical knowledge and be able to give us answers to questions we simply do not have the scope to deal with. Greg encouraged caution over its use in important tasks. Citing the example of the Amazon recruitment bot which saw fewer female applicants, and then becoming sexist as a result, to illustrate the dangers of putting machines in charge. He questions whether it would be sensible to admit machines into the moral community, given how differently a computer may actually experience morality.

The incredibly powerful GPT3 AI has recently been used to make a philosophy bot. But to what extent is it constructing sentences that sound like philosophy? Or could it be coming up with deep ideas by itself? I’ll let the headache sink in there…