Mathematical basis for concerns about AI

Mathematical basis for concerns about AI

Artificial intelligence (AI) is increasingly deployed around us and may have large potential benefits. But there are growing concerns about the unethical use of AI. Professor Anthony Davison, who holds the Chair of Statistics at EPFL, and colleagues in the UK, have tackled these questions from a mathematical point of view, focusing on commercial AI that seek to maximize profits.

One example is an insurance company using AI to find a strategy for deciding premiums for potential customers. The AI will choose from many potential strategies, some of which may be discriminatory or may otherwise misuse customer data in ways that later lead to severe penalties for the company. Ideally, unethical strategies such as these would be removed from the space of potential strategies beforehand, but the AI does not have a moral sense, so it cannot distinguish between ethical and unethical strategies.

In work published in Royal Society Open Science on 1 July 2020, Davison and his co-authors Heather Battey (Imperial College London), Nicholas Beale (Sciteb Limited) and Robert MacKay (University of Warwick), show that an AI is likely to pick an unethical strategy in many situations. They formulate their results as an “Unethical Optimization Principle”:

If an AI aims to maximize risk-adjusted return, then under mild conditions it is disproportionately likely to pick an unethical strategy unless the objective function allows sufficiently for this risk.

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Source: “Ethics and AI: an unethical optimization principle”, Orane Jecker, EPFL