{"type":"ScholarlyArticle","title":"A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges","authors":[{"name":"Usman Gohar","slug":"usman-gohar","given":"Usman","family":"Gohar","affiliation":"Iowa State University"},{"name":"Lu Cheng","slug":"lu-cheng","given":"Lu","family":"Cheng","affiliation":"University of Illinois Chicago"}],"abstract":"The widespread adoption of Machine Learning systems, especially in more decision-critical applications such as criminal sentencing and bank loans, has led to increased concerns about fairness implications. Algorithms and metrics have been developed to mitigate and measure these discriminations. More recently, works have identified a more challenging form of bias called intersectional bias, which encompasses multiple sensitive attributes, such as race and gender, together. In this survey, we review the state-of-the-art in intersectional fairness. We present a taxonomy for intersectional notions of fairness and mitigation. Finally, we identify the key challenges and provide researchers with guidelines for future directions.","doi":"10.24963/ijcai.2023/742","url":"https://ijcai.org/proceedings/2023/742","pdf":"https://ijcai.org/proceedings/2023/0742.pdf","markdown":"https://ijcai.org/proceedings/2023/742.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/742","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Survey Track","track":"Survey Track","number":742,"pages":{"first":6619,"last":6627},"keywords":["Survey: Natural Language Processing","Survey: AI Ethics, Trust, Fairness","Survey: Machine Learning","Survey: Multidisciplinary Topics and Applications"],"videos":[],"references":[{"position":1,"raw":"[Abbasi et al., 2021] Ahmed Abbasi, David Dobolyi, John P. Lalor, Richard G. Netemeyer, Kendall Smith, and Yi Yang. Construct- ing a psychometric testbed for fair natural language processing. In EMNLP, pages 3748-3758. ACL, 2021.","title":"Constructing a psychometric testbed for fair natural language processing","authors":"Ahmed Abbasi, David Dobolyi, John P Lalor, Richard G Netemeyer, Kendall Smith, Yi Yang","year":"2021","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Agarwal et al., 2018] Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach. A reduc- tions approach to fair classification. In ICML, pages 60-69.","title":"A reductions approach to fair classification","authors":"Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, Hanna Wallach","year":null,"venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"PMLR, 2018.","title":null,"authors":"Pmlr","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Akrami et al., 2011] Nazar Akrami, Bo Ekehammar, and Robin Bergh. Generalized prejudice: Common and specific compo- nents. Psychological Science, 22(1):57-59, 2011.","title":"Generalized prejudice: Common and specific components","authors":"Nazar Akrami, Bo Ekehammar, Robin Bergh","year":"2011","venue":"Psychological Science","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Angwin et al., 2016] Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. Machine bias. In Ethics of data and analytics, pages 254-264. Auerbach Publications, 2016.","title":"Machine bias","authors":"Julia Angwin, Jeff Larson, Surya Mattu, Lauren Kirchner","year":"2016","venue":"Ethics of data and analytics","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Barnabò et al., 2020] Giorgio Barnabò, Carlos Castillo, Michael Mathioudakis, and Sergio Celis. Intersectional affirmative action policies for top-k candidates selection. ArXiv, abs/2007.14775, 2020.","title":"Intersectional affirmative action policies for top-k candidates selection","authors":"Giorgio Barnabò, Carlos Castillo, Michael Mathioudakis, Sergio Celis","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Bierly, 1985] Margaret M. Bierly. Prejudice toward contemporary outgroups as a generalized attitude. Journal of Applied Social Psychology, 15:189-199, 1985.","title":"Prejudice toward contemporary outgroups as a generalized attitude","authors":"Margaret M Bierly","year":"1985","venue":"Journal of Applied Social Psychology","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Bose and Hamilton, 2019] Avishek Bose and William Hamilton. Compositional fairness constraints for graph embeddings. In ICML, pages 715-724. PMLR, 2019.","title":"Compositional fairness constraints for graph embeddings","authors":"Avishek Bose, William Hamilton","year":"2019","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Buolamwini and Gebru, 2018] Joy Buolamwini and Timnit Gebru. Gender shades: Intersectional accuracy disparities in commercial gender classification. In In FAT, pages 77-91. PMLR, 2018.","title":"Joy Buolamwini and Timnit Gebru. Gender shades: Intersectional accuracy disparities in commercial gender classification","authors":"Gebru Buolamwini","year":"2018","venue":"FAT","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Cabrera et al., 2019] Ángel Alexander Cabrera, Will Epperson, Fred Hohman, Minsuk Kahng, Jamie H. Morgenstern, and Duen Horng Chau. Fairvis: Visual analytics for discovering in- tersectional bias in machine learning. IEEE VAST, 2019.","title":"Fairvis: Visual analytics for discovering intersectional bias in machine learning","authors":"Alexander Ángel, Will Cabrera, Fred Epperson, Minsuk Hohman, Jamie H Kahng, Duen Morgenstern, Chau Horng","year":"2019","venue":"IEEE VAST","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Cachel et al., 2022] Kathleen Cachel, Elke A. Rundensteiner, and Lane Harrison. Mani-rank: Multiple attribute and intersectional group fairness for consensus ranking. IEEE ICDE, 2022.","title":"Mani-rank: Multiple attribute and intersectional group fairness for consensus ranking","authors":"Kathleen Cachel, Elke A Rundensteiner, Lane Harrison","year":"2022","venue":"IEEE ICDE","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Câmara et al., 2022] António Câmara, Nina Taneja, Tamjeed Azad, Emily Allaway, and Richard Zemel. Mapping the multilin- gual margins: Intersectional biases of sentiment analysis systems in English, Spanish, and Arabic. In LT-EDI. ACL, 2022.","title":"Mapping the multilingual margins: Intersectional biases of sentiment analysis systems in English, Spanish, and Arabic","authors":"António Câmara, Nina Taneja, Tamjeed Azad, Emily Allaway, Richard Zemel","year":"2022","venue":"LT-EDI. ACL","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Caton and Haas, 2020] Simon Caton and Christian Haas. Fairness in machine learning: A survey. ArXiv, abs/2010.04053, 2020.","title":"Fairness in machine learning: A survey","authors":"Haas Caton, Simon Caton, Christian Haas","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Chen et al., 2022] Zhenpeng Chen, J Zhang, Max Hort, Federica Sarro, and Mark Harman. Fairness testing: A comprehensive survey and analysis of trends. ArXiv, abs/2207.10223, 2022.","title":"Fairness testing: A comprehensive survey and analysis of trends","authors":"Zhenpeng Chen, J Zhang, Max Hort, Federica Sarro, Mark Harman","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Cheng et al., 2021] Lu Cheng, Kush R Varshney, and Huan Liu. Socially responsible ai algorithms: Issues, purposes, and chal- lenges. JAIR, 71:1137-1181, 2021.","title":"Socially responsible ai algorithms: Issues, purposes, and challenges","authors":"Lu Cheng, Kush R Varshney, Huan Liu","year":"2021","venue":"JAIR","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Cheng et al., 2022] Lu Cheng, Nayoung Kim, and Huan Liu. De- biasing word embeddings with nonlinear geometry. In COLING, pages 1286-1298, 2022.","title":"Debiasing word embeddings with nonlinear geometry","authors":"Lu Cheng, Nayoung Kim, Huan Liu","year":"2022","venue":"COLING","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Chung et al., 2020] Y. Chung, T. Kraska, N. Polyzotis, K. Tae, and S. Whang. Automated data slicing for model validation: A big data -ai integration approach. IEEE TKDE, 2020.","title":"Automated data slicing for model validation: A big data -ai integration approach","authors":"Y Chung, T Kraska, N Polyzotis, K Tae, S Whang","year":"2020","venue":"IEEE TKDE","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Cohen et al., 2009] Joel W Cohen, Steven B Cohen, and Jessica S Banthin. The medical expenditure panel survey: a national infor- mation resource to support healthcare cost research and inform policy and practice. Medical care, pages S44-S50, 2009.","title":"The medical expenditure panel survey: a national information resource to support healthcare cost research and inform policy and practice","authors":"Steven B Joel W Cohen, Jessica S Cohen, Banthin","year":"2009","venue":"Medical care","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Commission, 1978] Equal Employment Opportunity Commission. Guidelines on employee selection procedures. C.F.R., 29., 1978.","title":"Equal Employment Opportunity Commission. Guidelines on employee selection procedures","authors":null,"year":"1978","venue":"C.F.R","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Corbett-Davies et al., 2017] Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq. Algorithmic decision making and the cost of fairness. KDD, page 797-806. ACM, 2017.","title":"Algorithmic decision making and the cost of fairness","authors":"Corbett-Davies","year":"2017","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Cortez and Silva, 2008] Paulo Cortez and Alice Maria Gonc ¸alves Silva. Using data mining to predict secondary school student performance. EUROSIS-ETI, 2008.","title":"Using data mining to predict secondary school student performance","authors":"Cortez, Silva ;, Paulo Cortez, Alice Maria Gonc ¸alves Silva","year":"2008","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Crenshaw, 1989] Kimberle Crenshaw. Demarginalizing the inter- section of race and sex: A black feminist critique of antidiscrim- ination doctrine, feminist theory and antiracist politics. The Uni- versity of Chicago Legal Forum, 140:139-167, 1989.","title":"Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics","authors":"Kimberle Crenshaw, Crenshaw","year":"1989","venue":"The University of Chicago Legal Forum","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Dwork et al., 2012] Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. Fairness through awareness. In ITCS, page 214-226. ACM, 2012.","title":"Fairness through awareness","authors":"Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Richard Zemel","year":"2012","venue":"ITCS","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Dwork, 2006] Cynthia Dwork. Differential privacy. In ICALP 2006, volume 4052 of Lecture Notes in Computer Science, pages 1-12. Springer Verlag, July 2006.","title":"Differential privacy","authors":"Cynthia Dwork","year":"2006","venue":"ICALP 2006","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Foulds et al., 2018] James R. Foulds, Rashidul Islam, Kamrun Keya, and Shimei Pan. Bayesian modeling of intersectional fair- ness: The variance of bias. In SDM, 2018.","title":"Bayesian modeling of intersectional fairness: The variance of bias","authors":"James R Foulds, Rashidul Islam, Kamrun Keya, Shimei Pan","year":"2018","venue":"SDM","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Foulds et al., 2020] James R. Foulds, Rashidul Islam, Kamrun Na- her Keya, and Shimei Pan. An intersectional definition of fair- ness. In ICDE, pages 1918-1921, 2020.","title":"An intersectional definition of fairness","authors":"James R Foulds, Rashidul Islam, Kamrun Naher Keya, Shimei Pan","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Ghai et al., 2021] Bhavya Ghai, Md Naimul Hoque, and Klaus Mueller. Wordbias: An interactive visual tool for discovering intersectional biases encoded in word embeddings. In Extended Abstracts of CHI, pages 1-7, 2021.","title":"Wordbias: An interactive visual tool for discovering intersectional biases encoded in word embeddings","authors":"Bhavya Ghai, Md Naimul Hoque, Klaus Mueller","year":"2021","venue":"Extended Abstracts of CHI","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Ghosh et al., 2021] A. Ghosh, Lea Genuit, and Mary Reagan. Characterizing intersectional group fairness with worst-case comparisons. In AIDBEI, 2021.","title":"Characterizing intersectional group fairness with worst-case comparisons","authors":"A Ghosh, Lea Genuit, Mary Reagan","year":"2021","venue":"AIDBEI","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Gillen et al., 2018] Stephen Gillen, Christopher Jung, Michael Kearns, and Aaron Roth. Online learning with an unknown fair- ness metric. In NeurIPS, page 2605-2614, 2018.","title":"Online learning with an unknown fairness metric","authors":"Stephen Gillen, Christopher Jung, Michael Kearns, Aaron Roth","year":"2018","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Gjurković et al., 2021] Matej Gjurković, Mladen Karan, Iva Vuko- jević, Mihaela Bošnjak, and Jan Snajder. PANDORA talks: Per- sonality and demographics on Reddit. In SocialNLP, pages 138- 152. ACL, 2021.","title":"PANDORA talks: Personality and demographics on Reddit","authors":"Matej Gjurković, Mladen Karan, Iva Vukojević, Mihaela Bošnjak, Jan Snajder","year":"2021","venue":"SocialNLP","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Gohar et al., 2022] Usman Gohar, Sumon Biswas, and Hridesh Rajan. Towards understanding fairness and its composition in ensemble machine learning. arXiv preprint arXiv:2212.04593, 2022.","title":"Towards understanding fairness and its composition in ensemble machine learning","authors":"Usman Gohar, Sumon Biswas, Hridesh Rajan","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Gopalan et al., 2022] Parikshit Gopalan, Michael P Kim, Mihir A Singhal, and Shengjia Zhao. Low-degree multicalibration. In COLT, volume 178 of PMLR, pages 3193-3234, 2022.","title":"Low-degree multicalibration","authors":"Parikshit Gopalan, P Michael, Mihir A Kim, Shengjia Singhal, Zhao","year":"2022","venue":"COLT","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Guo and Caliskan, 2021] Wei Guo and Aylin Caliskan. Detect- ing emergent intersectional biases: Contextualized word embed- dings contain a distribution of human-like biases. In AIES, page 122-133. ACM, 2021.","title":"Detecting emergent intersectional biases: Contextualized word embeddings contain a distribution of human-like biases","authors":"Caliskan ; ] Guo, Guo Wei, Aylin Caliskan","year":"2021","venue":"AIES","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Hardt et al., 2016] Moritz Hardt, Eric Price, Eric Price, and Nati Srebro. Equality of opportunity in supervised learning. In NeurIPS, volume 29, 2016.","title":"Equality of opportunity in supervised learning","authors":"Moritz Hardt, Eric Price, Eric Price, Nati Srebro","year":"2016","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Harper and Konstan, 2015] F. Maxwell Harper and Joseph A. Kon- stan. The movielens datasets: History and context. ACM Trans. Interact. Intell. Syst., 5(4), 2015.","title":"The movielens datasets: History and context","authors":"F, Maxwell Harper, Joseph A Konstan","year":"2015","venue":"ACM Trans. Interact. Intell. Syst","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Hashimoto et al., 2018] Tatsunori B. Hashimoto, Megha Srivas- tava, Hongseok Namkoong, and Percy Liang. Fairness without demographics in repeated loss minimization. In ICML, 2018.","title":"Fairness without demographics in repeated loss minimization","authors":"B Tatsunori, Megha Hashimoto, Hongseok Srivastava, Percy Namkoong, Liang","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Hassan et al., 2021] Saad Hassan, Matt Huenerfauth, and Ce- cilia Ovesdotter Alm. Unpacking the interdependent systems of discrimination: Ableist bias in NLP systems through an intersec- tional lens. In EMNLP, pages 3116-3123. ACL, 2021.","title":"Unpacking the interdependent systems of discrimination: Ableist bias in NLP systems through an intersectional lens","authors":"Saad Hassan, Matt Huenerfauth, Cecilia Ovesdotter, Alm","year":"2021","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Hebert-Johnson et al., 2018] Ursula Hebert-Johnson, Michael Kim, Omer Reingold, and Guy Rothblum. Multicalibration: Calibration for the (Computationally-identifiable) masses. In ICML, volume 80, pages 1939-1948. PMLR, 2018.","title":"Multicalibration: Calibration for the (Computationally-identifiable) masses","authors":"Ursula Hebert-Johnson, Michael Kim, Omer Reingold, Guy Rothblum","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Huang et al., 2020] Xiaolei Huang, Linzi Xing, Franck Dernon- court, and Michael J. Paul. Multilingual Twitter corpus and base- lines for evaluating demographic bias in hate speech recognition. In LREC, pages 1440-1448. ELRA, 2020.","title":"Multilingual Twitter corpus and baselines for evaluating demographic bias in hate speech recognition","authors":"Xiaolei Huang, Linzi Xing, Franck Dernoncourt, Michael J Paul","year":"2020","venue":"LREC","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Islam et al., 2019] Rashidul Islam, Kamrun Naher Keya, Shimei Pan, and James Foulds. Mitigating demographic biases in social media-based recommender systems. KDD (Social Impact Track), 2019.","title":"Mitigating demographic biases in social media-based recommender systems","authors":"Islam","year":"2019","venue":"KDD (Social Impact Track)","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Jin et al., 2020] Zhongjun Jin, Mengjing Xu, Chenkai Sun, Abol- fazl Asudeh, and H. V. Jagadish. Mithracoverage: A system for investigating population bias for intersectional fairness. In ICDM, page 2721-2724. ACM, 2020.","title":"Mithracoverage: A system for investigating population bias for intersectional fairness","authors":"Jin","year":"2020","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Kang et al., 2022] Jian Kang, Tiankai Xie, Xintao Wu, Ross Ma- ciejewski, and Hanghang Tong. Infofair: Information-theoretic intersectional fairness. In IEEE Big Data, 2022.","title":"Infofair: Information-theoretic intersectional fairness","authors":"Jian Kang, Tiankai Xie, Xintao Wu, Ross Maciejewski, Hanghang Tong","year":"2022","venue":"IEEE Big Data","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Kearns et al., 2018] Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu. Preventing fairness gerrymandering: Audit- ing and learning for subgroup fairness. In ICML. PMLR, 2018.","title":"Preventing fairness gerrymandering: Auditing and learning for subgroup fairness","authors":"Michael Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu","year":"2018","venue":"ICML. PMLR","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Kim et al., 2018] Michael P. Kim, Omer Reingold, and Guy N. Rothblum. Fairness through computationally-bounded aware- ness. In NeurIPS, page 4847-4857, 2018.","title":"Fairness through computationally-bounded awareness","authors":"P Michael, Omer Kim, Guy N Reingold, Rothblum","year":"2018","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Kim et al., 2019] Michael P. Kim, Amirata Ghorbani, and James Zou. Multiaccuracy: Black-box post-processing for fairness in classification. In AIES, page 247-254. ACM, 2019.","title":"Multiaccuracy: Black-box post-processing for fairness in classification","authors":"P Michael, Amirata Kim, James Ghorbani, Zou","year":"2019","venue":"AIES","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Kiritchenko and Mohammad, 2018] Svetlana Kiritchenko and Saif Mohammad. Examining gender and race bias in two hundred sentiment analysis systems. In SemEval, pages 43-53, 2018.","title":"Examining gender and race bias in two hundred sentiment analysis systems","authors":"Mohammad ; Kiritchenko, Svetlana Kiritchenko, Saif Mohammad","year":"2018","venue":"SemEval","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Kirk et al., 2021] Hannah Rose Kirk, Yennie Jun, Haider Iqbal, Elias Benussi, Filippo Volpin, Frédéric A. Dreyer, Aleksandar Shtedritski, and Yuki M. Asano. Bias out-of-the-box: An em- pirical analysis of intersectional occupational biases in popular generative language models. In NeurIPS, 2021.","title":"Bias out-of-the-box: An empirical analysis of intersectional occupational biases in popular generative language models","authors":"Rose Hannah, Yennie Kirk, Haider Jun, Elias Iqbal, Filippo Benussi, Frédéric A Volpin, Aleksandar Dreyer, Yuki M Shtedritski, Asano","year":"2021","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Kohavi, 1996] Ron Kohavi. Scaling up the accuracy of naive- bayes classifiers: A decision-tree hybrid. In SIGKDD, KDD'96, page 202-207. AAAI Press, 1996.","title":"Scaling up the accuracy of naivebayes classifiers: A decision-tree hybrid","authors":"Ron Kohavi, Kohavi","year":"1996","venue":"SIGKDD, KDD'96","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Kong, 2022] Youjin Kong. Are \"intersectionally fair\" ai algo- rithms really fair to women of color? a philosophical analysis. FAccT '22, page 485-494. ACM, 2022.","title":"Youjin Kong. Are \"intersectionally fair\" ai algorithms really fair to women of color? a philosophical analysis","authors":"Kong","year":"2022","venue":"FAccT '","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Kusner et al., 2017] Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. Counterfactual fairness. In NeurIPS, 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":51,"raw":"[Lahoti et al., 2020] Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, and Ed Chi. Fairness without demographics through adversarially reweighted learning. In NeurIPS, volume 33, pages 728-740, 2020.","title":"Fairness without demographics through adversarially reweighted learning","authors":"Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed Chi","year":"2020","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Lalor et al., 2022] John Lalor, Yi Yang, Kendall Smith, Nicole Forsgren, and Ahmed Abbasi. Benchmarking intersectional bi- ases in NLP. In NAACL, pages 3598-3609. ACL, 2022.","title":"Benchmarking intersectional biases in NLP","authors":"John Lalor, Yi Yang, Kendall Smith, Nicole Forsgren, Ahmed Abbasi","year":"2022","venue":"NAACL","doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Liu et al., 2015] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xi- aoou Tang. Deep learning face attributes in the wild. In ICCV, December 2015.","title":"Deep learning face attributes in the wild","authors":"Ziwei Liu, Ping Luo, Xiaogang Wang, Xiaoou Tang","year":"2015","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Martinez et al., 2021] Natalia L Martinez, Martin A Bertran, Afroditi Papadaki, Miguel Rodrigues, and Guillermo Sapiro. Blind pareto fairness and subgroup robustness. In ICML, PMLR, pages 7492-7501, 2021.","title":"Blind pareto fairness and subgroup robustness","authors":"Natalia L Martinez, Martin A Bertran, Afroditi Papadaki, Miguel Rodrigues, Guillermo Sapiro","year":"2021","venue":"ICML, PMLR","doi":null,"url":null,"internal_url":null},{"position":55,"raw":"[Mas-Colell et al., 1995] Andreu Mas-Colell, Michael D. Whin- ston, and Jerry R. Green. Microeconomic Theory. Oxford Uni- versity Press, New York, 1995.","title":"Microeconomic Theory","authors":"Andreu Mas-Colell, Michael D Whinston, Jerry R Green","year":"1995","venue":null,"doi":null,"url":null,"internal_url":null},{"position":56,"raw":"[May et al., 2019] Chandler May, Alex Wang, Shikha Bordia, Samuel Bowman, and Rachel Rudinger. On measuring social biases in sentence encoders. In 2019 NAACL, 2019.","title":"On measuring social biases in sentence encoders","authors":"May","year":"2019","venue":"NAACL","doi":null,"url":null,"internal_url":null},{"position":57,"raw":"[Mehrabi et al., 2021] Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. A survey on bias and fairness in machine learning. ACM Comput. Surv., 54(6), 2021.","title":"A survey on bias and fairness in machine learning","authors":"Mehrabi","year":"2021","venue":"ACM Comput. Surv","doi":null,"url":null,"internal_url":null},{"position":58,"raw":"[Molina and Loiseau, 2022] Mathieu Molina and Patrick Loiseau. Bounding and approximating intersectional fairness through marginal fairness. arXiv preprint arXiv:2206.05828, 2022.","title":"Bounding and approximating intersectional fairness through marginal fairness","authors":"Loiseau Molina, Mathieu Molina, Patrick Loiseau","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":59,"raw":"[Morina et al., 2019] Giulio Morina, Viktoriia Oliinyk, Julian Wa- ton, Ines Marusic, and Konstantinos Georgatzis. Auditing and achieving intersectional fairness in classification problems. CoRR, abs/1911.01468, 2019.","title":"Auditing and achieving intersectional fairness in classification problems","authors":"Giulio Morina, Viktoriia Oliinyk, Julian Waton, Ines Marusic, Konstantinos Georgatzis","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":60,"raw":"[Rawls, 2001] John Rawls. Justice as fairness: A restatement. Har- vard University Press, 2001.","title":"Justice as fairness: A restatement","authors":"John Rawls","year":"2001","venue":null,"doi":null,"url":null,"internal_url":null},{"position":61,"raw":"[Shannon, 1948] Claude E Shannon. A mathematical theory of communication. The Bell system technical journal, 27(3):379- 423, 1948.","title":"A mathematical theory of communication","authors":"Claude E Shannon","year":"1948","venue":"The Bell system technical journal","doi":null,"url":null,"internal_url":null},{"position":62,"raw":"[Shui et al., 2022] Changjian Shui, Gezheng Xu, Qi CHEN, Jiaqi Li, Charles Ling, Tal Arbel, Boyu Wang, and Christian Gagné. On learning fairness and accuracy on multiple subgroups. In NeurIPS, 2022.","title":"On learning fairness and accuracy on multiple subgroups","authors":"Changjian Shui, Gezheng Xu, Chen Qi, Jiaqi Li, Charles Ling, Tal Arbel, Boyu Wang, Christian Gagné","year":"2022","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":63,"raw":"[Steed and Caliskan, 2021] Ryan Steed and Aylin Caliskan. Image representations learned with unsupervised pre-training contain human-like biases. FAccT '21, page 701-713. ACM, 2021.","title":"Image representations learned with unsupervised pre-training contain human-like biases","authors":"Caliskan ; Steed, Ryan Steed, Aylin Caliskan","year":"2021","venue":"FAccT '","doi":null,"url":null,"internal_url":null},{"position":64,"raw":"[Subramanian et al., 2021] Shivashankar Subramanian, Xudong Han, Timothy Baldwin, Trevor Cohn, and Lea Frermann. Evalu- ating debiasing techniques for intersectional biases. In EMNLP, pages 2492-2498. ACL, 2021.","title":"Evaluating debiasing techniques for intersectional biases","authors":"Shivashankar Subramanian, Xudong Han, Timothy Baldwin, Trevor Cohn, Lea Frermann","year":"2021","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":65,"raw":"[Tan and Celis, 2019] Yi Chern Tan and L Elisa Celis. Assessing social and intersectional biases in contextualized word represen- tations. In NeurIPS, 32, 2019.","title":"Assessing social and intersectional biases in contextualized word representations","authors":"Yi Chern, Tan, L Elisa, Celis","year":"2019","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":66,"raw":"[Wang et al., 2022] Angelina Wang, Vikram V Ramaswamy, and Olga Russakovsky. Towards intersectionality in machine learn- ing: Including more identities, handling underrepresentation, and performing evaluation. In FAccT, page 336-349. ACM, 2022.","title":"Towards intersectionality in machine learning: Including more identities, handling underrepresentation, and performing evaluation","authors":"Angelina Wang, V Vikram, Olga Ramaswamy, Russakovsky","year":"2022","venue":"FAccT","doi":null,"url":null,"internal_url":null},{"position":67,"raw":"[Wightman, 1998] Linda F. Wightman. LSAC National Longitudi- nal Bar Passage Study. LSAC research report series. Law School Admission Council, 1998.","title":"LSAC National Longitudinal Bar Passage Study","authors":"Linda F Wightman","year":"1998","venue":null,"doi":null,"url":null,"internal_url":null},{"position":68,"raw":"[Yang et al., 2020a] Forest Yang, Mouhamadou Cisse, and Sanmi Koyejo. Fairness with overlapping groups; a probabilistic per- spective. In NeurIPS, volume 33, pages 4067-4078, 2020.","title":"Fairness with overlapping groups; a probabilistic perspective","authors":"Yang","year":"2020","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":69,"raw":"[Yang et al., 2020b] Ke Yang, Joshua R Loftus, and Julia Stoy- anovich. Causal intersectionality for fair ranking. 2020.","title":"Causal intersectionality for fair ranking","authors":"Yang","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":70,"raw":"[Yona and Rothblum, 2018] Gal Yona and Guy Rothblum. Proba- bly approximately metric-fair learning. In ICML, pages 5680- 5688. PMLR, 2018.","title":"Gal Yona and Guy Rothblum. Probably approximately metric-fair learning","authors":"Rothblum Yona","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":71,"raw":"[Zhang et al., 2017] Zhifei Zhang, Yang Song, and Hairong Qi. Age progression/regression by conditional adversarial autoen- coder. 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}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}