When Your Only Tool Is A Hammer

Abstract

It is no longer a hypothetical worry that artificial intelligence - more specifically, machine learning (ML) - can propagate the effects of pernicious bias in healthcare. To address these problems, some have proposed the development of 'algorithmic fairness' solutions. The primary goal of these solutions is to constrain the effect of pernicious bias with respect to a given outcome of interest as a function of one's protected identity (i.e., characteristics generally protected by civil or human rights legislation. The technical limitations of these solutions have been well-characterized. Ethically, the problematic implication - of developers, potentially, and end users - is that by virtue of algorithmic fairness solutions a model can be rendered 'objective' (i.e., free from the influence of pernicious bias). The ostensible neutrality of these solutions may unintentionally prompt new consequences for vulnerable groups by obscuring downstream problems due to the persistence of real-world bias.

Ähnliche Arbeiten