{"type":"ScholarlyArticle","title":"Supervised Algorithmic Fairness in Distribution Shifts: A Survey","authors":[{"name":"Minglai Shao","slug":"minglai-shao","given":"Minglai","family":"Shao","affiliation":"School of New Media and Communication , Tianjin University , China"},{"name":"Dong Li","slug":"dong-li","given":"Dong","family":"Li","affiliation":"College of Intelligence and Computing , Tianjin University , China"},{"name":"Chen Zhao","slug":"chen-zhao","given":"Chen","family":"Zhao","affiliation":"Department of Computer Science , Baylor University , USA"},{"name":"Xintao Wu","slug":"xintao-wu","given":"Xintao","family":"Wu","affiliation":"Electrical Engineering and Computer Science Department , University of Arkansas , USA"},{"name":"Yujie Lin","slug":"yujie-lin","given":"Yujie","family":"Lin","affiliation":"School of New Media and Communication , Tianjin University , China"},{"name":"Qin Tian","slug":"qin-tian","given":"Qin","family":"Tian","affiliation":"College of Intelligence and Computing , Tianjin University , China"}],"abstract":"Supervised fairness-aware machine learning under distribution shifts is an emerging field that addresses the challenge of maintaining equitable and unbiased predictions when faced with changes in data distributions from source to target domains.\r\nIn real-world applications, machine learning models are often trained on a specific dataset but deployed in environments where the data distribution may shift over time due to various factors. This shift can lead to unfair predictions, disproportionately affecting certain groups characterized by sensitive attributes, such as race and gender. In this survey, we provide a summary of various types of distribution shifts and comprehensively investigate existing methods based on these shifts, highlighting six commonly used approaches in the literature. Additionally, this survey lists publicly available datasets and evaluation metrics for empirical studies. We further explore the interconnection with related research fields, discuss the significant challenges, and identify potential directions for future studies.","doi":"10.24963/ijcai.2024/909","url":"https://ijcai.org/proceedings/2024/909","pdf":"https://ijcai.org/proceedings/2024/0909.pdf","markdown":"https://ijcai.org/proceedings/2024/909.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/909","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Survey Track","track":"Survey Track","number":909,"pages":{"first":8225,"last":8233},"keywords":["AI Ethics, Trust, Fairness: ETF: Fairness and diversity","AI Ethics, Trust, Fairness: ETF: Bias"],"videos":[],"references":[{"position":1,"raw":"[An et al., 2022] Bang An, Zora Che, Mucong Ding, and Furong Huang. Transferring fairness under distribu- tion shifts via fair consistency regularization. NeurIPS, 35:32582-32597, 2022.","title":"Bang An, Zora Che, Mucong Ding, and Furong Huang. Transferring fairness under distribution shifts via fair consistency regularization","authors":"An","year":"2022","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Arjovsky et al., 2019] Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. Invariant risk minimization. arXiv:1907.02893, 2019.","title":"Invariant risk minimization","authors":"Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, David Lopez-Paz","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Asuncion and Newman, 2007] Arthur Asuncion and David Newman. Uci machine learning repository, 2007.","title":"Arthur Asuncion and David Newman","authors":"Asuncion, Newman","year":"2007","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Biswas and Mukherjee, 2021] Arpita Biswas and Suvam Mukherjee. Ensuring fairness under prior probability shifts. In AIES, pages 414-424, 2021.","title":"Ensuring fairness under prior probability shifts","authors":"Arpita Biswas, Suvam Mukherjee","year":"2021","venue":"AIES","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen et al., 2022] Yatong Chen, Reilly Raab, Jialu Wang, and Yang Liu. Fairness transferability subject to bounded distribution shift. NeurIPS, 35:11266-11278, 2022.","title":"Fairness transferability subject to bounded distribution shift","authors":"Chen","year":"2022","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Corbett-Davies and Goel, 2018] Sam Corbett-Davies and Sharad Goel. The measure and mismeasure of fair- ness: A critical review of fair machine learning. arXiv:1808.00023, 2018.","title":"The measure and mismeasure of fairness: A critical review of fair machine learning","authors":"Corbett-Davies, Sam Goel, Sharad Corbett-Davies, Goel","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Coston et al., 2019] Amanda Coston, Karthikeyan Natesan Ramamurthy, Dennis Wei, Kush R Varshney, Skyler Speakman, Zairah Mustahsan, and Supriyo Chakraborty. Fair transfer learning with missing protected attributes. In AIES, pages 91-98, 2019.","title":"Fair transfer learning with missing protected attributes","authors":"Amanda Coston, Karthikeyan Natesan Ramamurthy, Dennis Wei, R Kush, Skyler Varshney, Zairah Speakman, Supriyo Mustahsan, Chakraborty","year":"2019","venue":"AIES","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Creager et al., 2020] Elliot Creager, David Madras, Toniann Pitassi, and Richard Zemel. Causal modeling for fairness in dynamical systems. In ICML. PMLR, 2020.","title":"Causal modeling for fairness in dynamical systems","authors":"Elliot Creager, David Madras, Toniann Pitassi, Richard Zemel","year":"2020","venue":"ICML. PMLR","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Creager et al., 2021] Elliot Creager, Jörn-Henrik Jacobsen, and Richard Zemel. Environment inference for invariant learning. In ICML, pages 2189-2200. PMLR, 2021.","title":"Environment inference for invariant learning","authors":"Elliot Creager, Jörn-Henrik Jacobsen, Richard Zemel","year":"2021","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Dai and Wang, 2022] Enyan Dai and Suhang Wang. Learn- ing fair graph neural networks with limited and private sensitive attribute information. IEEE Transactions on Knowledge and Data Engineering, 2022.","title":"Learning fair graph neural networks with limited and private sensitive attribute information","authors":"Wang ; ] Enyan Dai, Suhang Dai, Wang","year":"2022","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Deepak and Abraham, 2021] Parakkal Deepak and Savitha Sam Abraham. Fairlof: fairness in outlier detection. Data Science and Engineering, 2021.","title":"Parakkal Deepak and Savitha Sam Abraham. Fairlof: fairness in outlier detection","authors":"Abraham Deepak","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Dhamala et al., 2021] Jwala Dhamala, Tony Sun, Varun Ku- mar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei Chang, and Rahul Gupta. Bold: Dataset and metrics for measuring biases in open-ended language generation. In FAccT, pages 862-872, 2021.","title":"Bold: Dataset and metrics for measuring biases in open-ended language generation","authors":"Jwala Dhamala, Tony Sun, Varun Kumar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei Chang, Rahul Gupta","year":"2021","venue":"FAccT","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Dressel and Farid, 2018] Julia Dressel and Hany Farid. The accuracy, fairness, and limits of predicting recidivism. Sci- ence advances, 4(1):eaao5580, 2018.","title":"The accuracy, fairness, and limits of predicting recidivism","authors":"Farid Dressel, Julia Dressel, Hany Farid","year":"2018","venue":"Science advances","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Du and Wu, 2021] Wei Du and Xintao Wu. Fair and robust classification under sample selection bias. In CIKM, pages 2999-3003, 2021.","title":"Fair and robust classification under sample selection bias","authors":"Wei Du, Xintao Wu","year":"2021","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Dwork et al., 2012] Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference, 2012.","title":"Fairness through awareness","authors":"Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Richard Zemel","year":"2012","venue":"Proceedings of the 3rd innovations in theoretical computer science conference","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Giguere et al., 2022] Stephen Giguere, Blossom Metevier, Yuriy Brun, Bruno Castro da Silva, Philip S Thomas, and Scott Niekum. Fairness guarantees under demographic shift. In ICLR, 2022.","title":"Fairness guarantees under demographic shift","authors":"Giguere","year":"2022","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Han et al., 2023] Xiao Han, Lu Zhang, Yongkai Wu, and Shuhan Yuan. Achieving counterfactual fairness for anomaly detection. In PAKDD. Springer, 2023.","title":"Achieving counterfactual fairness for anomaly detection","authors":"Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan","year":"2023","venue":"PAKDD","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Hardt et al., 2016] Moritz Hardt, Eric Price, and Nati Sre- bro. Equality of opportunity in supervised learning. NeurIPS, 29, 2016.","title":"Equality of opportunity in supervised learning","authors":"Moritz Hardt, Eric Price, Nati Srebro","year":"2016","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Iosifidis and Ntoutsi, 2020] Vasileios Iosifidis and Eirini Ntoutsi. Fabboo-online fairness-aware learning under class imbalance. In International Conference on Discov- ery Science, pages 159-174. Springer, 2020.","title":"Vasileios Iosifidis and Eirini Ntoutsi. Fabboo-online fairness-aware learning under class imbalance","authors":"Ntoutsi Iosifidis","year":"2020","venue":"International Conference on Discovery Science","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Iosifidis et al., 2019] Vasileios Iosifidis, Thi Ngoc Han Tran, and Eirini Ntoutsi. Fairness-enhancing interven- tions in stream classification. In DEXA, pages 261-276. Springer, 2019.","title":"Fairness-enhancing interventions in stream classification","authors":"Vasileios Iosifidis, Thi Ngoc Han Tran, Eirini Ntoutsi","year":"2019","venue":"DEXA","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Kallus and Zhou, 2018] Nathan Kallus and Angela Zhou. Residual unfairness in fair machine learning from preju- diced data. In ICML, pages 2439-2448. PMLR, 2018.","title":"Residual unfairness in fair machine learning from prejudiced data","authors":"Zhou Kallus, Nathan Kallus, Angela Zhou","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Kärkkäinen and Joo, 2019] Kimmo Kärkkäinen and Jungseock Joo. Fairface: Face attribute dataset for balanced race, gender, and age. arXiv:1908.04913, 2019.","title":"Fairface: Face attribute dataset for balanced race, gender, and age","authors":"Joo Kärkkäinen, Kimmo Kärkkäinen, Jungseock Joo","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Koh et al., 2021] Pang Wei Koh, Shiori Sagawa, Hen- rik Marklund, Sang Michael Xie, Marvin Zhang, Ak- shay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al. Wilds: A bench- mark of in-the-wild distribution shifts. In ICML, pages 5637-5664. PMLR, 2021.","title":"Wilds: A benchmark of in-the-wild distribution shifts","authors":"Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao","year":"2021","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Kohavi and others, 1996] Ron Kohavi et al. Scaling up the accuracy of naive-bayes classifiers: A decision-tree hy- brid. In KDD, volume 96, pages 202-207, 1996.","title":"Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid","authors":"Ron Kohavi, Kohavi","year":"1996","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Kusner et al., 2017] Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. Counterfactual fairness. NeurIPS, 30, 2017.","title":"Counterfactual fairness","authors":"Matt J Kusner, Joshua Loftus, Chris Russell, Ricardo Silva","year":"2017","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Li et al., 2022] Shu Li, Jiong Yu, Xusheng Du, Yi Lu, and Rui Qiu. Fair outlier detection based on adversarial repre- sentation learning. Symmetry, 14(2):347, 2022.","title":"Fair outlier detection based on adversarial representation learning","authors":"Shu Li, Jiong Yu, Xusheng Du, Yi Lu, Rui Qiu","year":"2022","venue":"Symmetry","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Li et al., 2024] Yibo Li, Xiao Wang, Yujie Xing, Shaohua Fan, Ruijia Wang, Yaoqi Liu, and Chuan Shi. Graph fairness learning under distribution shifts. arXiv preprint arXiv:2401.16784, 2024.","title":"Graph fairness learning under distribution shifts","authors":"Yibo Li, Xiao Wang, Yujie Xing, Shaohua Fan, Ruijia Wang, Yaoqi Liu, Chuan Shi","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Lin et al., 2023] Yujie Lin, Chen Zhao, Minglai Shao, Xu- jiang Zhao, and Haifeng Chen. Adaptation speed analysis for fairness-aware causal models. In CIKM, 2023.","title":"Adaptation speed analysis for fairness-aware causal models","authors":"Yujie Lin, Chen Zhao, Minglai Shao, Xujiang Zhao, Haifeng Chen","year":"2023","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Lin et al., 2024] Yujie Lin, Chen Zhao, Minglai Shao, Baoluo Meng, Xujiang Zhao, and Haifeng Chen. To- wards counterfactual fairness-aware domain generaliza- tion in changing environments. IJCAI, 2024.","title":"Towards counterfactual fairness-aware domain generalization in changing environments","authors":"Yujie Lin, Chen Zhao, Minglai Shao, Baoluo Meng, Xujiang Zhao, Haifeng Chen","year":"2024","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2024/504"},{"position":30,"raw":"[Liu et al., 2021] Xiaofeng Liu, Bo Hu, Linghao Jin, Xu Han, Fangxu Xing, Jinsong Ouyang, Jun Lu, Georges EL Fakhri, and Jonghye Woo. Domain gener- alization under conditional and label shifts via variational bayesian inference. arXiv:2107.10931, 2021.","title":"Domain generalization under conditional and label shifts via variational bayesian inference","authors":"Xiaofeng Liu, Bo Hu, Linghao Jin, Xu Han, Fangxu Xing, Jinsong Ouyang, Jun Lu, Georges El Fakhri, Jonghye Woo","year":"2021","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2021/122"},{"position":31,"raw":"[Mandal et al., 2020] Debmalya Mandal, Samuel Deng, Suman Jana, Jeannette Wing, and Daniel J Hsu. Ensur- ing fairness beyond the training data. NeurIPS, 2020.","title":"Ensuring fairness beyond the training data","authors":"Mandal","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Newman, 2015] John Newman. Semantic shift. The Rout- ledge handbook of semantics, pages 266-280, 2015.","title":"Semantic shift. The Routledge handbook of semantics","authors":"John Newman","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Oh et al., 2022] Changdae Oh, Heeji Won, Junhyuk So, Taero Kim, Yewon Kim, Hosik Choi, and Kyungwoo Song. Learning fair representation via distributional con- trastive disentanglement. In KDD, 2022.","title":"Learning fair representation via distributional contrastive disentanglement","authors":"Oh","year":"2022","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Pearl, 2009] Judea Pearl. Causality. Cambridge university press, 2009.","title":"Judea Pearl. Causality","authors":"Pearl","year":"2009","venue":null,"doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Pham et al., 2023] Thai-Hoang Pham, Xueru Zhang, and Ping Zhang. Fairness and accuracy under domain general- ization. arXiv:2301.13323, 2023.","title":"Fairness and accuracy under domain generalization","authors":"Pham","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Rezaei et al., 2020] Ashkan Rezaei, Rizal Fathony, Omid Memarrast, and Brian Ziebart. Fairness for robust log loss classification. In AAAI, 2020.","title":"Fairness for robust log loss classification","authors":"Ashkan Rezaei, Rizal Fathony, Omid Memarrast, Brian Ziebart","year":"2020","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Rezaei et al., 2021] Ashkan Rezaei, Anqi Liu, Omid Memarrast, and Brian D Ziebart. Robust fairness under covariate shift. In AAAI, 2021.","title":"Robust fairness under covariate shift","authors":"Ashkan Rezaei, Anqi Liu, Omid Memarrast, Brian D Ziebart","year":"2021","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Roh et al., 2023] Yuji Roh, Kangwook Lee, Steven Euijong Whang, and Changho Suh. Improving fair training under correlation shifts. ICML, 2023.","title":"Improving fair training under correlation shifts","authors":"Yuji Roh, Kangwook Lee, Steven Euijong Whang, Changho Suh","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Saerens et al., 2002] Marco Saerens, Patrice Latinne, and Christine Decaestecker. Adjusting the outputs of a classi- fier to new a priori probabilities: a simple procedure. Neu- ral computation, 14(1):21-41, 2002.","title":"Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure","authors":"Marco Saerens, Patrice Latinne, Christine Decaestecker","year":"2002","venue":"Neural computation","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Sagawa et al., 2019] Shiori Sagawa, Pang Wei Koh, Tat- sunori B Hashimoto, and Percy Liang. Distribution- ally robust neural networks for group shifts: On the im- portance of regularization for worst-case generalization. arXiv:1911.08731, 2019.","title":"Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization","authors":"Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, Percy Liang","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Schrouff et al., 2022] Jessica Schrouff, Natalie Harris, Sanmi Koyejo, Ibrahim M Alabdulmohsin, Eva Schnider, Krista Opsahl-Ong, Alexander Brown, Subhrajit Roy, Di- ana Mincu, Christina Chen, et al. Diagnosing failures of fairness transfer across distribution shift in real-world medical settings. NeurIPS, 35:19304-19318, 2022.","title":"Diagnosing failures of fairness transfer across distribution shift in real-world medical settings","authors":"Jessica Schrouff, Natalie Harris, Sanmi Koyejo, Ibrahim M Alabdulmohsin, Eva Schnider, Krista Opsahl-Ong, Alexander Brown, Subhrajit Roy, Diana Mincu, Christina Chen","year":"2022","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Schumann et al., 2019] Candice Schumann, Xuezhi Wang, Alex Beutel, Jilin Chen, Hai Qian, and Ed H Chi. Transfer of machine learning fairness across domains. arXiv:1906.09688, 2019.","title":"Transfer of machine learning fairness across domains","authors":"Candice Schumann, Xuezhi Wang, Alex Beutel, Jilin Chen, Hai Qian, Ed H Chi","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Shekhar et al., 2021] Shubhranshu Shekhar, Neil Shah, and Leman Akoglu. Fairod: Fairness-aware outlier detection. In AIES, pages 210-220, 2021.","title":"Fairod: Fairness-aware outlier detection","authors":"Shekhar","year":"2021","venue":"AIES","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Shimodaira, 2000] Hidetoshi Shimodaira. Improving pre- dictive inference under covariate shift by weighting the log-likelihood function. Journal of statistical planning and inference, 2000.","title":"Improving predictive inference under covariate shift by weighting the log-likelihood function","authors":"Hidetoshi Shimodaira","year":"2000","venue":"Journal of statistical planning and inference","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Shpitser and Pearl, 2008] Ilya Shpitser and Judea Pearl. Complete identification methods for the causal hierarchy. JMLR, 2008.","title":"Ilya Shpitser and Judea Pearl. Complete identification methods for the causal hierarchy","authors":"Pearl Shpitser","year":"2008","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Singh et al., 2021] Harvineet Singh, Rina Singh, Vishwali Mhasawade, and Rumi Chunara. Fairness violations and mitigation under covariate shift. In FAccT, 2021.","title":"Harvineet Singh, Rina Singh, Vishwali Mhasawade, and Rumi Chunara. Fairness violations and mitigation under covariate shift","authors":"Singh","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Song et al., 2021] Hanyu Song, Peizhao Li, and Hongfu Liu. Deep clustering based fair outlier detection. In SIGKDD, pages 1481-1489, 2021.","title":"Deep clustering based fair outlier detection","authors":"Hanyu Song, Peizhao Li, Hongfu Liu","year":"2021","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Taskesen et al., 2020] Bahar Taskesen, Viet Anh Nguyen, Daniel Kuhn, and Jose Blanchet. A distributionally robust approach to fair classification. arXiv:2007.09530, 2020.","title":"A distributionally robust approach to fair classification","authors":"Bahar Taskesen, Anh Viet, Daniel Nguyen, Jose Kuhn, Blanchet","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Thomas et al., 2019] Philip S Thomas, Bruno Castro da Silva, Andrew G Barto, Stephen Giguere, Yuriy Brun, and Emma Brunskill. Preventing undesirable behavior of in- telligent machines. Science, 366(6468):999-1004, 2019.","title":"Preventing undesirable behavior of intelligent machines","authors":"Bruno Philip S Thomas, Andrew G Castro Da Silva, Stephen Barto, Yuriy Giguere, Emma Brun, Brunskill","year":"2019","venue":"Science","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Wang et al., 2003] Ke Wang, Senqiang Zhou, Chee Ada Fu, and Jeffrey Xu Yu. Mining changes of classification by correspondence tracing. In Proceedings of the 2003 SIAM International Conference on Data Mining. SIAM, 2003.","title":"Mining changes of classification by correspondence tracing","authors":"Ke Wang, Senqiang Zhou, Chee, Ada Fu, Jeffrey Xu, Yu","year":"2003","venue":"Proceedings of the 2003 SIAM International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Widmer and Kubat, 1996] Gerhard Widmer and Miroslav Kubat. Learning in the presence of concept drift and hid- den contexts. Machine learning, 23:69-101, 1996.","title":"Learning in the presence of concept drift and hidden contexts","authors":"Kubat Widmer, Gerhard Widmer, Miroslav Kubat","year":"1996","venue":"Machine learning","doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Yamazaki et al., 2007] Keisuke Yamazaki, Motoaki Kawanabe, Sumio Watanabe, Masashi Sugiyama, and Klaus-Robert Müller. Asymptotic bayesian generalization error when training and test distributions are different. In ICML, pages 1079-1086, 2007.","title":"Asymptotic bayesian generalization error when training and test distributions are different","authors":"Keisuke Yamazaki, Motoaki Kawanabe, Sumio Watanabe, Masashi Sugiyama, Klaus-Robert Müller","year":"2007","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Yao and Liu, 2023] Yuanshun Yao and Yang Liu. Un- derstanding unfairness via training concept influence. arXiv:2306.17828, 2023.","title":"Understanding unfairness via training concept influence","authors":"Liu Yao, Yuanshun Yao, Yang Liu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Yoon et al., 2020] Taeho Yoon, Jaewook Lee, and Woojin Lee. Joint transfer of model knowledge and fairness over domains using wasserstein distance. IEEE Access, 8:123783-123798, 2020.","title":"Joint transfer of model knowledge and fairness over domains using wasserstein distance","authors":"Taeho Yoon, Jaewook Lee, Woojin Lee","year":"2020","venue":"IEEE Access","doi":null,"url":null,"internal_url":null},{"position":55,"raw":"[Zemel et al., 2013] Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork. Learning fair represen- tations. In ICML. PMLR, 2013.","title":"Learning fair representations","authors":"Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, Cynthia Dwork","year":"2013","venue":"ICML. PMLR","doi":null,"url":null,"internal_url":null},{"position":56,"raw":"[Zhang et al., 2017] Zhifei Zhang, Yang Song, and Hairong Qi. Age progression/regression by conditional adversarial autoencoder. In CVPR, pages 5810-5818, 2017.","title":"Age progression/regression by conditional adversarial autoencoder","authors":"Zhang","year":"2017","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":57,"raw":"[Zhao et al., 2021] Chen Zhao, Feng Chen, and Bhavani Thuraisingham. Fairness-aware online meta-learning. In SIGKDD, pages 2294-2304, 2021.","title":"Fairness-aware online meta-learning","authors":"Chen Zhao, Feng Chen, Bhavani Thuraisingham","year":"2021","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":58,"raw":"[Zhao et al., 2022] Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, and Feng Chen. Adaptive fairness- aware online meta-learning for changing environments. In SIGKDD, pages 2565-2575, 2022.","title":"Adaptive fairnessaware online meta-learning for changing environments","authors":"Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Feng Chen","year":"2022","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":59,"raw":"[Zhao et al., 2023a] Chen Zhao, Kai Jiang, Xintao Wu, Hao- liang Wang, Latifur Khan, Christan Grant, and Feng Chen. Fairness-aware domain generalization under covariate and dependence shifts. arXiv:2311.13816, 2023.","title":"Fairness-aware domain generalization under covariate and dependence shifts","authors":"Zhao","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":60,"raw":"[Zhao et al., 2023b] Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Christan Grant, and Feng Chen. To- wards fair disentangled online learning for changing envi- ronments. In SIGKDD, pages 3480-3491, 2023.","title":"Towards fair disentangled online learning for changing environments","authors":"Zhao","year":"2023","venue":"SIGKDD","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}