Multi-Dimensional Model Integrity and Responsibility Assessment Index and Scoring Framework

TL;DR

MIRAI is a comprehensive evaluation framework that assesses tabular AI models across multiple dimensions like fairness and robustness, providing a unified score for responsible model selection.

Contribution

The paper introduces MIRAI, a unified index that combines multiple responsible AI metrics into a single score for tabular models, enabling better cross-dimensional comparison.

Findings

Higher predictive performance does not always mean better overall integrity.

Simpler models can outperform complex architectures in balancing multiple dimensions.

MIRAI facilitates responsible model selection in regulated environments.

Abstract

Artificial intelligence in high-stakes tabular domains cannot be evaluated by predictive performance alone, yet current practice still assesses explainability, fairness, robustness, privacy, and sustainability mostly in isolation. We propose the Model Integrity and Responsibility Assessment Index (MIRAI), a unified evaluation framework that measures tabular models across these five dimensions under a controlled comparison setting and aggregates them into a single score. MIRAI combines established metrics through normalized and direction-aligned dimension scores, which enables direct comparison across models with different architectural and computational profiles. Experiments on healthcare, financial, and socioeconomic datasets show that higher predictive performance does not necessarily imply better overall integrity and responsibility. In several cases, simpler models achieve a stronger cross-dimensional balance than more complex deep tabular architectures. MIRAI provides a compact and practical basis for responsible model selection in regulated settings.