Academic literature on the topic 'ML fairness'
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Journal articles on the topic "ML fairness"
Weinberg, Lindsay. "Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approaches." Journal of Artificial Intelligence Research 74 (May 6, 2022): 75–109. http://dx.doi.org/10.1613/jair.1.13196.
Full textBærøe, Kristine, Torbjørn Gundersen, Edmund Henden, and Kjetil Rommetveit. "Can medical algorithms be fair? Three ethical quandaries and one dilemma." BMJ Health & Care Informatics 29, no. 1 (2022): e100445. http://dx.doi.org/10.1136/bmjhci-2021-100445.
Full textYanjun Li, Yanjun Li, Huan Huang Yanjun Li, Qiang Geng Huan Huang, Xinwei Guo Qiang Geng, and Yuyu Yuan Xinwei Guo. "Fairness Measures of Machine Learning Models in Judicial Penalty Prediction." 網際網路技術學刊 23, no. 5 (2022): 1109–16. http://dx.doi.org/10.53106/160792642022092305019.
Full textAlotaibi, Dalha Alhumaidi, Jianlong Zhou, Yifei Dong, Jia Wei, Xin Janet Ge, and Fang Chen. "Quantile Multi-Attribute Disparity (QMAD): An Adaptable Fairness Metric Framework for Dynamic Environments." Electronics 14, no. 8 (2025): 1627. https://doi.org/10.3390/electronics14081627.
Full textGhosh, Bishwamittra, Debabrota Basu, and Kuldeep S. Meel. "Algorithmic Fairness Verification with Graphical Models." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (2022): 9539–48. http://dx.doi.org/10.1609/aaai.v36i9.21187.
Full textKuzucu, Selim, Jiaee Cheong, Hatice Gunes, and Sinan Kalkan. "Uncertainty as a Fairness Measure." Journal of Artificial Intelligence Research 81 (October 13, 2024): 307–35. http://dx.doi.org/10.1613/jair.1.16041.
Full textWeerts, Hilde, Florian Pfisterer, Matthias Feurer, et al. "Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML." Journal of Artificial Intelligence Research 79 (February 17, 2024): 639–77. http://dx.doi.org/10.1613/jair.1.14747.
Full textSingh, Vivek K., and Kailash Joshi. "Integrating Fairness in Machine Learning Development Life Cycle: Fair CRISP-DM." e-Service Journal 14, no. 2 (2022): 1–24. http://dx.doi.org/10.2979/esj.2022.a886946.
Full textMakhlouf, Karima, Sami Zhioua, and Catuscia Palamidessi. "On the Applicability of Machine Learning Fairness Notions." ACM SIGKDD Explorations Newsletter 23, no. 1 (2021): 14–23. http://dx.doi.org/10.1145/3468507.3468511.
Full textZhou, Zijian, Xinyi Xu, Rachael Hwee Ling Sim, Chuan Sheng Foo, and Bryan Kian Hsiang Low. "Probably Approximate Shapley Fairness with Applications in Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 5 (2023): 5910–18. http://dx.doi.org/10.1609/aaai.v37i5.25732.
Full textDissertations / Theses on the topic "ML fairness"
Kaplan, Caelin. "Compromis inhérents à l'apprentissage automatique préservant la confidentialité." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4045.
Full textBook chapters on the topic "ML fairness"
Steif, Ken. "People-based ML Models: Algorithmic Fairness." In Public Policy Analytics. CRC Press, 2021. http://dx.doi.org/10.1201/9781003054658-7.
Full textd’Aloisio, Giordano, Antinisca Di Marco, and Giovanni Stilo. "Democratizing Quality-Based Machine Learning Development through Extended Feature Models." In Fundamental Approaches to Software Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30826-0_5.
Full textRoberts-Licklider, Karen, and Theodore Trafalis. "Fairness in Optimization and ML: A Survey Part 2." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-81010-7_17.
Full textGonçalves, Rafael, Filipe Gouveia, Inês Lynce, and José Fragoso Santos. "Proxy Attribute Discovery in Machine Learning Datasets via Inductive Logic Programming." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-90653-4_17.
Full textSilva, Inês Oliveira e., Carlos Soares, Inês Sousa, and Rayid Ghani. "Systematic Analysis of the Impact of Label Noise Correction on ML Fairness." In Lecture Notes in Computer Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8391-9_14.
Full textChopra, Deepti, and Roopal Khurana. "Bias and Fairness in Ml." In Introduction to Machine Learning with Python. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815124422123010012.
Full textZhang, Wenbin, Zichong Wang, Juyong Kim, et al. "Individual Fairness Under Uncertainty." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230621.
Full textAndrae, Silvio. "Fairness and Bias in Machine Learning Models for Credit Decisions." In Advances in Finance, Accounting, and Economics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-8186-1.ch001.
Full textMuralidhar, L. B., N. Sathyanarayana, H. R. Swapna, Sheetal V. Hukkeri, and P. H. Reshma Sultana. "Machine Learning Advancing Diversity Equity and Inclusion in Data-Driven HR Practices." In Advances in Human Resources Management and Organizational Development. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-0149-5.ch019.
Full textCohen-Inger, Nurit, Guy Rozenblatt, Seffi Cohen, Lior Rokach, and Bracha Shapira. "FairUS - UpSampling Optimized Method for Boosting Fairness." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240585.
Full textConference papers on the topic "ML fairness"
Sabuncuoglu, Alpay, and Carsten Maple. "Towards Proactive Fairness Monitoring of ML Development Pipelines." In 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion). IEEE, 2025. https://doi.org/10.1109/citrexcompanion65208.2025.10981495.
Full textShe, Yining, Sumon Biswas, Christian Kästner, and Eunsuk Kang. "FairSense: Long-Term Fairness Analysis of ML-Enabled Systems." In 2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE). IEEE, 2025. https://doi.org/10.1109/icse55347.2025.00159.
Full textHertweck, Corinna, Michele Loi, and Christoph Heitz. "Group Fairness Refocused: Assessing the Social Impact of ML Systems." In 2024 11th IEEE Swiss Conference on Data Science (SDS). IEEE, 2024. http://dx.doi.org/10.1109/sds60720.2024.00034.
Full textLi, Zhiwei, Carl Kesselman, Mike D’Arcy, Michael Pazzani, and Benjamin Yizing Xu. "Deriva-ML: A Continuous FAIRness Approach to Reproducible Machine Learning Models." In 2024 IEEE 20th International Conference on e-Science (e-Science). IEEE, 2024. http://dx.doi.org/10.1109/e-science62913.2024.10678671.
Full textYu, Normen, Luciana Carreon, Gang Tan, and Saeid Tizpaz-Niari. "FairLay-ML: Intuitive Debugging of Fairness in Data-Driven Social-Critical Software." In 2025 IEEE/ACM 47th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). IEEE, 2025. https://doi.org/10.1109/icse-companion66252.2025.00016.
Full textRobles Herrera, Salvador, Verya Monjezi, Vladik Kreinovich, Ashutosh Trivedi, and Saeid Tizpaz-Niari. "Predicting Fairness of ML Software Configurations." In PROMISE '24: 20th International Conference on Predictive Models and Data Analytics in Software Engineering. ACM, 2024. http://dx.doi.org/10.1145/3663533.3664040.
Full textSmith, Jessie J., Michael Madaio, Robin Burke, and Casey Fiesler. "Pragmatic Fairness: Evaluating ML Fairness Within the Constraints of Industry." In FAccT '25: The 2025 ACM Conference on Fairness, Accountability, and Transparency. ACM, 2025. https://doi.org/10.1145/3715275.3732040.
Full textMakhlouf, Karima, Sami Zhioua, and Catuscia Palamidessi. "Identifiability of Causal-based ML Fairness Notions." In 2022 14th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2022. http://dx.doi.org/10.1109/cicn56167.2022.10008263.
Full textBaresi, Luciano, Chiara Criscuolo, and Carlo Ghezzi. "Understanding Fairness Requirements for ML-based Software." In 2023 IEEE 31st International Requirements Engineering Conference (RE). IEEE, 2023. http://dx.doi.org/10.1109/re57278.2023.00046.
Full textSilva, Bruno Pires M., and Lilian Berton. "Analyzing the Trade-off Between Fairness and Model Performance in Supervised Learning: A Case Study in the MIMIC dataset." In Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2025. https://doi.org/10.5753/sbcas.2025.6994.
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