The Utility of "Even if..." Semifactual Explanation to Optimise Positive Outcomes
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.
Code (1)
Tasks
2kMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
The Utility of “Even if” Semifactual Explanation to Optimise Positive Outcomes
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…
Semifactual Explanations for Reinforcement Learning
Reinforcement Learning (RL) is a learning paradigm in which the agent learns from its environment through trial and error. Deep reinforcement learning (DRL) algorithms represent the agent's policies using neural networks…
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)"Even if ..." -- Diverse Semifactual Explanations of Reject
Machine learning based decision making systems applied in safety critical areas require reliable high certainty predictions. For this purpose, the system can be extended by an reject option which allows the system to rej…
BIG-bench Machine LearningConformal PredictionDecision MakingExplainable Artificial Intelligence (XAI)Counterfactual and Semifactual Explanations in Abstract Argumentation: Formal Foundations, Complexity and Computation
Explainable Artificial Intelligence and Formal Argumentation have received significant attention in recent years. Argumentation-based systems often lack explainability while supporting decision-making processes. Counterf…
Abstract ArgumentationcounterfactualDecision MakingExplainable artificial intelligenceEven-if Explanations: Formal Foundations, Priorities and Complexity
EXplainable AI has received significant attention in recent years. Machine learning models often operate as black boxes, lacking explainability and transparency while supporting decision-making processes. Local post-hoc …
counterfactualDecision Making