Position: Fair Representations Cannot Hold What They Promise

This position paper argues that {\sl fair representations cannot hold what they promise}. Fair representation learning has been a significant trend in research on fairness for machine learning models over the past decade. The main idea of fair representation learning is to provide a data preprocessing mechanism which can facilitate fairness for downstream tasks, and research papers in this area often insinuate a fairness guarantee for all downstream tasks without further qualification. However, provably this cannot be satisfied by any non-degenerate representation. We argue that such over-promising can be harmful in downstream applications and that the responsibility for fairness cannot safely be outsourced to a representation provider in the way current research seems to suggest. As a minimal requirement for a way forward, we suggest that newly proposed fairness representations should be accompanied by compatibility tests that would allow a user to verify whether the representation will actually guarantee fairness for the task at hand.