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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2605.14550 |
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| _version_ | 1866918501206196224 |
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| author | Nguyen, Phuc Truong Loc Do, Thanh Hung Nguyen, Truong Thanh Hung Cao, Hung |
| author_facet | Nguyen, Phuc Truong Loc Do, Thanh Hung Nguyen, Truong Thanh Hung Cao, Hung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14550 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multi-Dimensional Model Integrity and Responsibility Assessment Index and Scoring Framework Nguyen, Phuc Truong Loc Do, Thanh Hung Nguyen, Truong Thanh Hung Cao, Hung Machine Learning 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. |
| title | Multi-Dimensional Model Integrity and Responsibility Assessment Index and Scoring Framework |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.14550 |