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Counterfactual Explanations in AI: Bridging Black Boxes With Ethical and Human Understanding
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Author(s): Baghavathi Priya S. (Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, India), Sri Surya Shobith Kamisetty (Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, India), Aravapalli Sohan Avaneesh Guptha (Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, India)and Nagireddy Kavyasree (Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, India)
Copyright: 2027
Pages: 36
Source title:
Encyclopedia of Modern Artificial Intelligence
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Founding Editor-in-Chief, Information Resources Management Journal (IRMJ), USA)
DOI: 10.4018/406020
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Abstract
This article presents the novel field of counterfactual explanations in Artificial Intelligence (AI) as a human-focused approach to improve model interpretability. It starts with the black-box problem in advanced AI systems and points out the increasing necessity for explainability in social, juridical, and technical domains. Counterfactuals are introduced as native “what-if” cases, examining their cognitive and philosophical basis, formal definition, and properties like proximity, sparsity, plausibility, and actionability. The chapter compares and contrasts counterfactuals with other explainability techniques such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), with examples in finance, healthcare, criminal justice, hiring, and education. Technical, ethical, and deployment issues such as human-centered design, regulatory compliance, and fairness auditing are explored. Directions of the future include causal reasoning, multimodal counterfactuals, and hybridizing with foundation models.
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