Robust Counterfactual Explanations under Model Multiplicity Using Multi-Objective Optimization
arXiv:2501.05795v4 Announce Type: replace-cross Abstract: In recent years, explainability in machine learning has gained importance. In this context, counterfactual explanation (CE), which is an explanation method that uses examples, has attracted attention. However, it has been pointed out that CE is not robust when there are multiple machine-learning models with similar accuracy. These problems are important when using machine learning to make safe decisions. In this paper, we propose robust CEs that introduce a new viewpoint -- Pareto improvement -- and a method that uses multi-objective op
The increasing deployment of AI in critical decision-making contexts necessitates robust explainability methods to ensure safety and trustworthiness, driving research in areas like counterfactual explanations.
Ensuring the robustness of AI explainability methods, particularly counterfactual explanations, is crucial for fostering trust, enabling regulatory compliance, and preventing unintended consequences in high-stakes applications.
This research introduces a method for more robust counterfactual explanations under model multiplicity, addressing a key limitation that has previously hindered the reliability of explainable AI.
- · AI developers
- · Regulatory bodies
- · Industries using AI for critical decisions
- · AI systems lacking explainability
- · Organizations deploying black-box models without rigorous validation
More reliable and trustworthy AI systems due to improved explainability.
Increased adoption of AI in sensitive domains like finance, healthcare, and defense where explainability is paramount.
Potential for new regulatory frameworks built around standardized, robust explainability metrics.
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Read at arXiv cs.AI