EXAGREE: Mitigating Explanation Disagreement with Stakeholder-Aligned Models

TL;DR

EXAGREE is a framework that resolves explanation disagreements in machine learning models by selecting stakeholder-aligned explanations, improving faithfulness, plausibility, and fairness without sacrificing accuracy.

Contribution

It introduces a novel two-stage method combining differentiable attribution and sorting to select models aligned with stakeholder explanations, addressing explanation conflict issues.

Findings

Improves faithfulness, plausibility, and fairness over baselines

Maintains task accuracy while enhancing explanation quality

Demonstrates robustness across six real-world datasets

Abstract

Conflicting explanations, arising from different attribution methods or model internals, limit the adoption of machine learning models in safety-critical domains. We turn this disagreement into an advantage and introduce EXplanation AGREEment (EXAGREE), a two-stage framework that selects a Stakeholder-Aligned Explanation Model (SAEM) from a set of similar-performing models. The selection maximizes Stakeholder-Machine Agreement (SMA), a single metric that unifies faithfulness and plausibility. EXAGREE couples a differentiable mask-based attribution network (DMAN) with monotone differentiable sorting, enabling gradient-based search inside the constrained model space. Experiments on six real-world datasets demonstrate simultaneous gains of faithfulness, plausibility, and fairness over baselines, while preserving task accuracy. Extensive ablation studies, significance tests, and case studies confirm the robustness and feasibility of the method in practice.