Attributing Fair Decisions with Attention Interventions
  The widespread use of Artificial Intelligence (AI) in consequential domains,
such as healthcare and parole decision-making systems, has drawn intense
scrutiny on the fairness of these methods. However, ensuring fairness is often
insufficient as the rationale for a contentious decision needs to be audited,
understood, and defended. We propose that the attention mechanism can be used
to ensure fair outcomes while simultaneously providing feature attributions to
account for how a decision was made. Toward this goal, we design an
attention-based model that can be leveraged as an attribution framework. It can
identify features responsible for both performance and fairness of the model
through attention interventions and attention weight manipulation. Using this
attribution framework, we then design a post-processing bias mitigation
strategy and compare it with a suite of baselines. We demonstrate the
versatility of our approach by conducting experiments on two distinct data
types, tabular and textual.
