What Is a Proxy and Why Is It a Problem?

Concerns with so-called ‘proxies’ have become ubiquitous in recent debates about algorithmic fairness. Yet it is far from clear what makes something a proxy and why it poses a problem. In this talk, Solon Barocas will differentiate between two different notions of proxies: One concerned with the choice of prediction target and the other concerned with the choice of input features. In the first case, the concern is that a chosen target can be a systematically biased measure — that is, a poor proxy — for the true outcome of interest and that even accurately predicting the target can be unfair. In the second case, the concern seems to be that even when a legally proscribed feature is not provided directly as an input into a machine learning model, discrimination on that basis may persist because non-proscribed features are correlated with — that is, serve as a proxy for — the proscribed feature. After disentangling these two different notions, Barocas will show that it can be surprisingly difficult, in both cases, to pin down whether something is a proxy and whether that should be cause for concern.

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On-site registration will not be permitted. Walk-in entry will be denied.