TraCE: Trajectory Counterfactual Explanation Scores
Counterfactual explanations, and their associated algorithmic recourse, are typically leveraged to understand, explain, and potentially alter a prediction coming from a black-box classifier. In this paper, we propose to extend the use of counterfactuals to evaluate progress in sequential decision making tasks. To this end, we introduce a model-agnostic modular framework, TraCE (Trajectory Counterfactual Explanation) scores, which is able to distill and condense progress in highly complex scenarios into a single value. We demonstrate TraCE's utility across domains by showcasing its main properties in two case studies spanning healthcare and climate change.
Code (1)
Tasks
counterfactualCounterfactual ExplanationDecision MakingSequential Decision MakingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Improving Model Understanding and Trust with Counterfactual Explanations of Model Confidence
In this paper, we show that counterfactual explanations of confidence scores help users better understand and better trust an AI model's prediction in human-subject studies. Showing confidence scores in human-agent inter…
counterfactualCounterfactual ExplanationmodelExplaining Model Confidence Using Counterfactuals
Displaying confidence scores in human-AI interaction has been shown to help build trust between humans and AI systems. However, most existing research uses only the confidence score as a form of communication. As confide…
counterfactualCounterfactual ExplanationmodelTRACE: A Conversational Framework for Sustainable Tourism Recommendation with Agentic Counterfactual Explanations
Traditional conversational travel recommender systems primarily optimize for user relevance and convenience, often reinforcing popular, overcrowded destinations and carbon-intensive travel choices. To address this, we pr…
Attribution-Scores and Causal Counterfactuals as Explanations in Artificial Intelligence
In this expository article we highlight the relevance of explanations for artificial intelligence, in general, and for the newer developments in {\em explainable AI}, referring to origins and connections of and among dif…
Logical ReasoningManagementCounterfactual Explanations for Face Forgery Detection via Adversarial Removal of Artifacts
Highly realistic AI generated face forgeries known as deepfakes have raised serious social concerns. Although DNN-based face forgery detection models have achieved good performance, they are vulnerable to latest generati…
Adversarial Attackcounterfactual