提出统一评估AI责任的评分框架，涵盖公平、可解释等四维度。
RAISE: A Unified Framework for Responsible AI Scoring and Evaluation
- 构建统一框架，从公平性、可解释性等四维量化模型责任表现。
- 实测显示：Transformer虽公平可解释但能耗极高，无模型全优。
- 适合关注AI伦理与多维度评估的研究者和开发者使用。
随着AI系统进入高风险领域，评估需超越预测准确率，涵盖可解释性、公平性、鲁棒性和可持续性。本文提出RAISE（负责任AI评分与评估）框架，量化模型在上述四个维度的表现，并整合为单一综合责任得分。我们在金融、医疗和社科领域的结构化数据集上评估了三种深度学习模型：多层感知机（MLP）、表格残差网络（Tabular ResNet）和特征标记变换器（Feature Tokenizer Transformer）。结果揭示关键权衡：MLP在可持续性和鲁棒性上表现优异，变换器在可解释性和公平性上领先但环境成本极高，而表格式残差网络展现出平衡性能。这表明单一模型无法在所有责任标准上全面占优，凸显多维度评估对负责任模型选择的重要性。代码已开源：https://github.com/raise-framework/raise。
原文摘要 · Abstract (English)
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.
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