提出评估负责任AI指标可靠性的方法指南。
The Quest for Reliable Metrics of Responsible AI
- 基于推荐系统公平性指标的鲁棒性研究，提炼出通用设计原则。
- 提出一套非穷尽的负责任AI指标开发指南，提升评估可信度。
- 适合关注AI伦理评估、算法可解释性的研究人员参考。
人工智能（AI）的发展，包括科学领域的人工智能（AIS），应遵循负责任AI的原则。尽管进展常通过评估指标量化，但对这些指标自身鲁棒性和可靠性评估的研究仍较少。本文回顾了针对推荐系统公平性指标鲁棒性的前期工作，将其核心发现总结为一系列非穷尽的负责任AI指标开发指南。该指南适用于广泛的AI应用，包括AIS，旨在帮助构建更可信、稳健的评估体系。
原文摘要 · Abstract (English)
The development of Artificial Intelligence (AI), including AI in Science (AIS), should be done following the principles of responsible AI. Progress in responsible AI is often quantified through evaluation metrics, yet there has been less work on assessing the robustness and reliability of the metrics themselves. We reflect on prior work that examines the robustness of fairness metrics for recommender systems as a type of AI application and summarise their key takeaways into a set of non-exhaustive guidelines for developing reliable metrics of responsible AI. Our guidelines apply to a broad spectrum of AI applications, including AIS.
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