Justifications derived from inconsistent case bases using authoritativeness

Post hoc analyses are used to provide interpretable explanations for machine learning predictions made by an opaque model. We modify a top-level model (AF-CBA) that uses case-based argumentation as such a post hoc analysis. AF-CBA justifies model predictions on the basis of an argument graph constructed using precedents from a case base. The effectiveness of this approach is limited when faced with an inconsistent case base, which are frequently encountered in practice. Reducing an inconsistent case base to a consistent subset is possible but undesirable. By altering the approach’s definition of best precedent to include an additional criterion based on an expression of authoritativeness, we allow AF-CBA to handle inconsistent case bases. We experiment with four different expressions of authoritativeness using three different data sets in order to evaluate their effect on the explanations generated in terms of the average number of precedents and the number of inconsistent a fortiori forcing relations.