Generating Counterfactual Explanations Using Cardinality Constraints
Providing explanations about how machine learning algorithms work and/or make particular predictions is one of the main tools that can be used to improve their trusworthiness, fairness and robustness. Among the most intuitive type of explanations are counterfactuals, which are examples that differ from a given point only in the prediction target and some set of features, presenting which features need to be changed in the original example to flip the prediction for that example. However, such counterfactuals can have many different features than the original example, making their interpretation difficult. In this paper, we propose to explicitly add a cardinality constraint to counterfactual generation limiting how many features can be different from the original example, thus providing more interpretable and easily understantable counterfactuals.
Code (0)
등록된 구현이 없습니다.
Tasks
counterfactualFairnessMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions. An intriguing class of explanations is through counterfactuals, hypothetical examples that show pe…
BIG-bench Machine LearningcounterfactualDiversityPoint ProcessesFGCE: Feasible Group Counterfactual Explanations for Auditing Fairness
This paper introduces the first graph-based framework for generating group counterfactual explanations to audit model fairness, a crucial aspect of trustworthy machine learning. Counterfactual explanations are instrument…
counterfactualFairnessGenerating Causally Compliant Counterfactual Explanations using ASP
This research is focused on generating achievable counterfactual explanations. Given a negative outcome computed by a machine learning model or a decision system, the novel CoGS approach generates (i) a counterfactual so…
AttributecounterfactualFeature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations
Interpretable machine learning seeks to understand the reasoning process of complex black-box systems that are long notorious for lack of explainability. One flourishing approach is through counterfactual explanations, w…
counterfactualDiversityfeature selectionInterpretable Machine Learning+2Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
To construct interpretable explanations that are consistent with the original ML model, counterfactual examples---showing how the model's output changes with small perturbations to the input---have been proposed. This pa…
BIG-bench Machine LearningcounterfactualDecision Making