What we talk about when we talk about fair AI

Fionntán O’Donnell – BBC News Labs

Courtesy of Laura Amaya from the Noun Project

This is an exceptionally good non-technical overview of fairness, accountability and transparency in AI. Each issue in turn is systematically disassembled and examined.  It is particularly strong on accountability, bringing out clearly that it can only rest on human agency and social and legal context. ‘My algorithm made me do it’ has roughly the same moral and intellectual depth as ‘a big boy made me do it’.

I have one minor, but not unimportant, quibble about the section on fairness. The first item on the suggested checklist is ‘Does the system fit within the company’s ethics?’ That is altogether too narrow a formulation, both in principle and in practice. It’s wrong in practice because there is no particular reason to suppose that a company’s (or any other organisation’s) ethics can be relied on to impose any meaningful standards. But it’s also wrong in principle: the relevant scope of ethical standards is not the producers of an algorithm, but the much larger set of people who use it or have it applied to them.

But that’s a detail. Overall, the combination of clear thinking and practical application makes this well worth reading.

Leave a Reply

Your email address will not be published. Required fields are marked *