The Quest for Reliable Metrics of Responsible AI
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 14 references that are in the map. First posted on arXiv 29 Oct 2025.
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References · 8
- 2022A Survey on the Fairness of Recommender Systemscited by 102
- 2023A Survey on Popularity Bias in Recommender Systemscited by 42
- 2020Citation Recommendation: Approaches and Datasetscited by 36
- 2022Scientific Paper Recommendation Systems: a Literature Review of recent Publicationscited by 10
- 2023Evaluation Measures of Individual Item Fairness for Recommender Systems: A Critical Studycited by 5
- 2021Overview of the TREC 2020 Fair Ranking Trackcited by 4
- 2025Stairway to Fairness: Connecting Group and Individual Fairnesscited by 3
- 2025Joint Evaluation of Fairness and Relevance in Recommender Systems with Pareto Frontiercited by 2
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