What will it take to generate fairness-preserving explanations?
This work addresses the problem of ensuring fairness in explanations for stakeholders using black-box models, but it is incremental as it highlights issues without presenting a new solution.
The paper investigates whether explanations of black-box models preserve fairness properties, finding that explanation algorithms can ignore or obscure critical fairness aspects, leading to incorrect or misleading explanations. It proposes future research directions for generating fairness-preserving explanations.
In situations where explanations of black-box models may be useful, the fairness of the black-box is also often a relevant concern. However, the link between the fairness of the black-box model and the behavior of explanations for the black-box is unclear. We focus on explanations applied to tabular datasets, suggesting that explanations do not necessarily preserve the fairness properties of the black-box algorithm. In other words, explanation algorithms can ignore or obscure critical relevant properties, creating incorrect or misleading explanations. More broadly, we propose future research directions for evaluating and generating explanations such that they are informative and relevant from a fairness perspective.