The Utility of "Even if..." Semifactual Explanation to Optimise Positive Outcomes
This work addresses a gap in Explainable AI by focusing on optimizing positive outcomes for users, such as in loan applications, though it is incremental as it builds on existing XAI methods.
The paper tackles the problem of explaining positive outcomes from automated systems by introducing semifactual explanations, which use 'even if...' reasoning to optimize user benefits without crossing decision boundaries, and shows through a user study that people find these explanations more useful than counterfactuals for loan acceptance scenarios.
When users receive either a positive or negative outcome from an automated system, Explainable AI (XAI) has almost exclusively focused on how to mutate negative outcomes into positive ones by crossing a decision boundary using counterfactuals (e.g., \textit{"If you earn 2k more, we will accept your loan application"}). Here, we instead focus on \textit{positive} outcomes, and take the novel step of using XAI to optimise them (e.g., \textit{"Even if you wish to half your down-payment, we will still accept your loan application"}). Explanations such as these that employ "even if..." reasoning, and do not cross a decision boundary, are known as semifactuals. To instantiate semifactuals in this context, we introduce the concept of \textit{Gain} (i.e., how much a user stands to benefit from the explanation), and consider the first causal formalisation of semifactuals. Tests on benchmark datasets show our algorithms are better at maximising gain compared to prior work, and that causality is important in the process. Most importantly however, a user study supports our main hypothesis by showing people find semifactual explanations more useful than counterfactuals when they receive the positive outcome of a loan acceptance.