The Utility of "Even if..." Semifactual Explanation to Optimise Positive
  Outcomes
Eoin M. Kenny Weipeng Huang
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.