探索负责任人工智能的可靠衡量标准

摘要

人工智能（包括科学人工智能）的发展应遵循负责任人工智能的原则。负责任人工智能的进展常通过评估指标来量化，但针对指标本身稳健性与可靠性的研究仍较欠缺。本文回顾了先前关于推荐系统（作为人工智能应用的一种形式）公平性指标稳健性的研究，并将其核心发现总结为一套非穷尽性的指导原则，用于制定可靠的责任人工智能评估指标。这些指导原则适用于包括科学人工智能在内的广泛人工智能应用领域。

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.

探索负责任人工智能的可靠衡量标准

The Quest for Reliable Metrics of Responsible AI

摘要

Support