{"ID":2852229,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.18559","arxiv_id":"2510.18559","title":"RAISE: A Unified Framework for Responsible AI Scoring and Evaluation","abstract":"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.","short_abstract":"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 th...","url_abs":"https://arxiv.org/abs/2510.18559","url_pdf":"https://arxiv.org/pdf/2510.18559v1","authors":"[\"Loc Phuc Truong Nguyen\",\"Hung Thanh Do\"]","published":"2025-10-21T12:15:13Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.CE\",\"cs.CY\"]","methods":"[\"Transformer\"]","has_code":false,"code_links":[{"ID":607976,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2852229,"paper_url":"https://arxiv.org/abs/2510.18559","paper_title":"RAISE: A Unified Framework for Responsible AI Scoring and Evaluation","repo_url":"https://github.com/raise-framework/raise","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}