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 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 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 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 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 textSreerama, Jeevan, and Gowrisankar Krishnamoorthy. "Ethical Considerations in AI Addressing Bias and Fairness in Machine Learning Models." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 1, no. 1 (2022): 130–38. http://dx.doi.org/10.60087/jklst.vol1.n1.p138.
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 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 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 textKothai, G., S. Nandhagopal, P. Harish, S. Sarankumar, and S. Vidhya. "Transforming Data Visualization With AI and ML." In Advances in Business Information Systems and Analytics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-6537-3.ch007.
Full textBendoukha, Adda-Akram, Nesrine Kaaniche, Aymen Boudguiga, and Renaud Sirdey. "FairCognizer: A Model for Accurate Predictions with Inherent Fairness Evaluation." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240592.
Full textWang, Song, Jing Ma, Lu Cheng, and Jundong Li. "Fair Few-Shot Learning with Auxiliary Sets." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230556.
Full textSunitha, K. "Ethical Issues, Fairness, Accountability, and Transparency in AI/ML." In Handbook of Research on Applications of AI, Digital Twin, and Internet of Things for Sustainable Development. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6821-0.ch007.
Full textConference papers on the topic "ML fairness"
Hertweck, 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 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 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 textEyuboglu, Sabri, Karan Goel, Arjun Desai, et al. "Model ChangeLists: Characterizing Updates to ML Models." In FAccT '24: The 2024 ACM Conference on Fairness, Accountability, and Transparency. ACM, 2024. http://dx.doi.org/10.1145/3630106.3659047.
Full textWexler, James, Mahima Pushkarna, Sara Robinson, Tolga Bolukbasi, and Andrew Zaldivar. "Probing ML models for fairness with the what-if tool and SHAP." In FAT* '20: Conference on Fairness, Accountability, and Transparency. ACM, 2020. http://dx.doi.org/10.1145/3351095.3375662.
Full textBlili-Hamelin, Borhane, and Leif Hancox-Li. "Making Intelligence: Ethical Values in IQ and ML Benchmarks." In FAccT '23: the 2023 ACM Conference on Fairness, Accountability, and Transparency. ACM, 2023. http://dx.doi.org/10.1145/3593013.3593996.
Full textHeidari, Hoda, Michele Loi, Krishna P. Gummadi, and Andreas Krause. "A Moral Framework for Understanding Fair ML through Economic Models of Equality of Opportunity." In FAT* '19: Conference on Fairness, Accountability, and Transparency. ACM, 2019. http://dx.doi.org/10.1145/3287560.3287584.
Full textSmith, Jessie J., Saleema Amershi, Solon Barocas, Hanna Wallach, and Jennifer Wortman Vaughan. "REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research." In FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency. ACM, 2022. http://dx.doi.org/10.1145/3531146.3533122.
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