Resolving Disagreement Problems in Explainable Artifi- cial Intelligence Through Multi-Criteria Decision Analysis

TMLR Paper6923 Authors

Abstract: Post-hoc explanation methods are critical for building trust in complex black-box artificial intelligence (AI) models; however, they often suffer from the disagreement problem, which provides conflicting explanations for the same prediction. This inconsistency undermines reliability and poses a significant barrier to adoption in high-stakes domains that demand trustworthiness and transparency. To address this, we move beyond the search for a single best method and instead propose a principled, preference-driven framework for selecting the best suitable explanation technique for a given context: \emph{which specific post-hoc explanation methods to use and when?} We formalize this selection process as a Multi-Criteria Decision Analysis (MCDA) problem. Our framework evaluates a set of state-of-the-art post-hoc explanation methods (e.g., LIME, SHAP, and Anchor) against six explanation evaluation metrics: fidelity, identity, stability, separability, faithfulness, and consistency. We then apply a suite of established MCDA techniques such as Simple Additive Weighting (SAW), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Elimination and Choice Translating Reality (ELECTRE I) to aggregate these evaluations based on user-defined priorities. By comparing the rankings produced by these diverse decision logics across multiple predictive models and real-world datasets, we demonstrate not only how to select the optimal explanation method under different priority scenarios (e.g., favoring fidelity vs. stability) but also how to expose critical trade-offs that are invisible to simpler aggregation approaches. Our work provides a robust, transparent, and adaptable methodology for resolving explanation disagreement, enabling practitioners to make more justifiable choices about explainability.

Submission Type: Long submission (more than 12 pages of main content)

Assigned Action Editor: ~Fabio_Stella1

Submission Number: 6923