Evaluating Explainability in Machine Learning Predictions Through Explainer-Agnostic Metrics

Authors: CRISTIAN ENRIQUE MUNOZ VILLALOBOS, Kleyton Da Costa, Bernardo Modenesi, Adriano Koshiyama
Primary area: interpretability and explainable AI
Keywords: Explainable AI, Explainability Metrics, Machine Learning, Fairness

Abstract:
Artificial intelligence (AI) continues to transform industries and research at an accelerated pace, bringing forth numerous challenges related to transparency and accountability in AI-driven decision-making. Decision-makers and stakeholders require not only a clear understanding of how these systems generate predictions but also assurance that these processes are conducted ethically and responsibly. These challenges highlight a critical need for effective tools to evaluate and enhance the interpretability of AI models. To address this gap, we propose a new set of explainer-sensitive metrics aimed at evaluating the interpretability of AI models in the context of specific explainers. By focusing on global and local feature importance, as well as surrogate models, our metrics capture key elements such as feature stability, fluctuations in prediction behavior, and contrasts in feature relevance across conditional subsets. By quantifying these complex dynamics as clear scalar measures, we offer a structured framework for assessing model transparency, fairness, and robustness. We demonstrate the practical utility of our approach through case studies on a set of benchmark datasets, revealing deeper insights into model interpretability that facilitate more informed decision-making among AI developers and stakeholders. Ultimately, our work aims to foster AI systems that are not only technically reliable but also transparent, fair, and accountable, thereby advancing the development of ethical AI practices.

Decision: reject

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