Explainable AI as evidence of fair decisions.

This paper will propose that explanations are valuable to those impacted by a model's decisions (model patients) to the extent that they provide evidence that a past adverse decision was unfair. Under this proposal, we should favor models and explainability methods which generate counterfactuals of two types. The first type of counterfactual is evidence of fairness: a set of states under the control of the patient which (if changed) would have led to a beneficial decision.