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.

翻译：当用户从自动化系统中获得积极或消极结果时，可解释人工智能（XAI）几乎只关注如何通过反事实（例如，“如果你多赚2000英镑，我们将接受你的贷款申请”）越过决策边界，将消极结果转化为积极结果。本文则聚焦于**积极**结果，并创新性地运用XAI对其进行优化（例如，“即使你希望将首付减半，我们仍将接受你的贷款申请”）。此类采用“即使……”推理且不越决策边界的解释被称为半事实。为在此场景中实例化半事实，我们引入了**增益**（即用户从解释中获得的收益程度）概念，并提出了半事实的首个因果形式化框架。基准数据集测试表明，与先前研究相比，我们的算法能更有效地最大化增益，且因果性在此过程中至关重要。然而，最重要的是，一项用户研究验证了我们的核心假设：当用户获得贷款获批的积极结果时，他们发现半事实解释比反事实解释更有用。