追寻负责任人工智能的可靠度量标准

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原则。当前，负责任人工智能的进展常通过评估指标来量化，但针对这些指标自身稳健性与可靠性的研究尚显不足。本文通过审视推荐系统（作为人工智能应用范例）公平性指标稳健性的既有研究，将其核心发现凝练为一套非穷尽性指南，用于制定可靠的责任人工智能评估标准。这些指南适用于包括科学人工智能在内的广泛人工智能应用场景。

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.

追寻负责任人工智能的可靠度量标准

The Quest for Reliable Metrics of Responsible AI

摘要

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