Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levels

• URL: http://arxiv.org/abs/2405.17642v1
• Date: Mon, 27 May 2024 20:32:09 GMT
• Title: Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levels
• Authors: Patryk Wielopolski, Oleksii Furman, Jerzy Stefanowski, Maciej Zięba,
• Abstract summary: Counterfactual Explanations (CFs) have emerged as a promising technique within Explainable AI (xAI) We introduce a novel unified approach for generating Local, Group-wise, and Global Counterfactual Explanations for differentiable classification models. Our work significantly advances the interpretability and accountability of AI models, marking a step forward in the pursuit of transparent AI.
• Score: 2.675793767640172
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Growing regulatory and societal pressures demand increased transparency in AI, particularly in understanding the decisions made by complex machine learning models. Counterfactual Explanations (CFs) have emerged as a promising technique within Explainable AI (xAI), offering insights into individual model predictions. However, to understand the systemic biases and disparate impacts of AI models, it is crucial to move beyond local CFs and embrace global explanations, which offer a~holistic view across diverse scenarios and populations. Unfortunately, generating Global Counterfactual Explanations (GCEs) faces challenges in computational complexity, defining the scope of "global," and ensuring the explanations are both globally representative and locally plausible. We introduce a novel unified approach for generating Local, Group-wise, and Global Counterfactual Explanations for differentiable classification models via gradient-based optimization to address these challenges. This framework aims to bridge the gap between individual and systemic insights, enabling a deeper understanding of model decisions and their potential impact on diverse populations. Our approach further innovates by incorporating a probabilistic plausibility criterion, enhancing actionability and trustworthiness. By offering a cohesive solution to the optimization and plausibility challenges in GCEs, our work significantly advances the interpretability and accountability of AI models, marking a step forward in the pursuit of transparent AI.

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