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Sunday, July 25, 2021

Should we be concerned that the decisions of AIs are inscrutable?

John Zerilli
Originally published 14 June 21

Here is an excerpt:

However, there’s a danger of carrying reliabilist thinking too far. Compare a simple digital calculator with an instrument designed to assess the risk that someone convicted of a crime will fall back into criminal behaviour (‘recidivism risk’ tools are being used all over the United States right now to help officials determine bail, sentencing and parole outcomes). The calculator’s outputs are so dependable that an explanation of them seems superfluous – even for the first-time homebuyer whose mortgage repayments are determined by it. One might take issue with other aspects of the process – the fairness of the loan terms, the intrusiveness of the credit rating agency – but you wouldn’t ordinarily question the engineering of the calculator itself.

That’s utterly unlike the recidivism risk tool. When it labels a prisoner as ‘high risk’, neither the prisoner nor the parole board can be truly satisfied until they have some grasp of the factors that led to it, and the relative weights of each factor. Why? Because the assessment is such that any answer will necessarily be imprecise. It involves the calculation of probabilities on the basis of limited and potentially poor-quality information whose very selection is value-laden.

But what if systems such as the recidivism tool were in fact more like the calculator? For argument’s sake, imagine a recidivism risk-assessment tool that was basically infallible, a kind of Casio-cum-Oracle-of-Delphi. Would we still expect it to ‘show its working’?

This requires us to think more deeply about what it means for an automated decision system to be ‘reliable’. It’s natural to think that such a system would make the ‘right’ recommendations, most of the time. But what if there were no such thing as a right recommendation? What if all we could hope for were only a right way of arriving at a recommendation – a right way of approaching a given set of circumstances? This is a familiar situation in law, politics and ethics. Here, competing values and ethical frameworks often produce very different conclusions about the proper course of action. There are rarely unambiguously correct outcomes; instead, there are only right ways of justifying them. This makes talk of ‘reliability’ suspect. For many of the most morally consequential and controversial applications of ML, to know that an automated system works properly just is to know and be satisfied with its reasons for deciding.