Nguyen L, Do H (2025)
Publication Type: Conference contribution
Publication year: 2025
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 16366 LNAI
Pages Range: 453-460
Conference Proceedings Title: Lecture Notes in Computer Science
Event location: Modena, ITA
ISBN: 9783032135612
DOI: 10.1007/978-3-032-13562-9_35
As AI systems enter high-stakes domains, evaluation must extend beyond predictive accuracy to include explainability, fairness, robustness, and sustainability. We introduce RAISE (Responsible AI Scoring and Evaluation), a unified framework that quantifies model performance across these four dimensions and aggregates them into a single, holistic Responsibility Score. We evaluated three deep learning models: a Multilayer Perceptron (MLP), a Tabular ResNet, and a Feature Tokenizer Transformer, on structured datasets from finance, healthcare, and socioeconomics. Our findings reveal critical trade-offs: the MLP demonstrated strong sustainability and robustness, the Transformer excelled in explainability and fairness at a very high environmental cost, and the Tabular ResNet offered a balanced profile. These results underscore that no single model dominates across all responsibility criteria, highlighting the necessity of multi-dimensional evaluation for responsible model selection. Our implementation is available at: https://github.com/raise-framework/raise.
APA:
Nguyen, L., & Do, H. (2025). RAISE: A Unified Framework for Responsible AI Scoring and Evaluation. In Catalin Dima, Angelo Ferrando, Vadim Malvone (Eds.), Lecture Notes in Computer Science (pp. 453-460). Modena, ITA: Springer Science and Business Media Deutschland GmbH.
MLA:
Nguyen, Loc, and Hung Do. "RAISE: A Unified Framework for Responsible AI Scoring and Evaluation." Proceedings of the 26th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2025, Modena, ITA Ed. Catalin Dima, Angelo Ferrando, Vadim Malvone, Springer Science and Business Media Deutschland GmbH, 2025. 453-460.
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