Ethics in AI Lunchtime Seminar - Wednesday 1st February at 12:30pm (GMT)
Attendance is via registration only, which can be found here.
With guest speaker Seth Lazar (ANU)
Optimism about our ability to enhance societal decision-making by leaning on Machine Learning (ML) for cheap, accurate predictions has palled in recent years, as these ‘cheap’ predictions have come at significant social cost, contributing to systematic harms suffered by already disadvantaged populations. But what precisely goes wrong when ML goes wrong? We argue that, as well as more obvious concerns about the downstream effects of ML-based decision-making, there can be moral grounds for the criticism of these predictions themselves. We introduce and defend a theory of predictive justice, according to which differential model performance for systematically disadvantaged groups can be grounds for moral criticism of the model, independently of its downstream effects. As well as helping resolve some urgent disputes around algorithmic fairness, this theory points the way to a novel dimension of epistemic ethics, related to the recently discussed category of doxastic wrong.
The seminar will run for one hour, from 12:30pm-1:30pm. This seminar will run in a hybrid format, allowing guests to attend in person or online.