핵심 개념
The author proposes a two-stage algorithm to find cost-efficient multi-instance counterfactual explanations, addressing the need for explanations that cover multiple instances. The approach involves grouping instances and computing tailored explanations for each group.
초록
The content discusses the importance of transparent AI systems and the rise of explainable AI (XAI). It introduces the concept of counterfactual explanations, which recommend actionable changes to alter system outputs. The focus is on multi-instance counterfactual explanations, where a single explanation applies to multiple instances simultaneously. The proposed two-stage algorithm aims to find groups of instances and provide cost-efficient multi-instance counterfactual explanations. Experimental results show the effectiveness of the proposed method compared to existing approaches.
Key points:
- Importance of transparency in AI systems.
- Definition and significance of counterfactual explanations.
- Introduction of multi-instance counterfactual explanations.
- Proposal of a two-stage algorithm for efficient multi-instance explanations.
- Evaluation through experiments on different datasets.
통계
"35 features for 1467 unique employees"
"30000 data records of customers"
"20798 law school admission records"
인용구
"Counterfactual explanations constitute among the most popular methods for analyzing predictions."
"Transparency creates trust and assists decision-makers in understanding where it is safe to deploy AI systems."
"Our proposed evolutionary algorithm achieves excellent performance across all settings."