{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:28:53Z","timestamp":1778200133642,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":88,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,6]],"date-time":"2022-10-06T00:00:00Z","timestamp":1665014400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,6]]},"DOI":"10.1145\/3551624.3555286","type":"proceedings-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T11:16:15Z","timestamp":1666005375000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Tackling Documentation Debt: A Survey on Algorithmic Fairness Datasets"],"prefix":"10.1145","author":[{"given":"Alessandro","family":"Fabris","sequence":"first","affiliation":[{"name":"University of Padua, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefano","family":"Messina","sequence":"additional","affiliation":[{"name":"University of Padua, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gianmaria","family":"Silvello","sequence":"additional","affiliation":[{"name":"University of Padua, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gian Antonio","family":"Susto","sequence":"additional","affiliation":[{"name":"University of Padua, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3314266"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1147\/JRD.2019.2942288"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287582"},{"key":"e_1_3_2_1_4_1","volume-title":"Advances in Neural Information Processing Systems, H.\u00a0Wallach, H.\u00a0Larochelle, A.\u00a0Beygelzimer, F.\u00a0d'Alch\u00e9-Buc, E.\u00a0Fox, and R.\u00a0Garnett (Eds.). Vol.\u00a032. Curran Associates","author":"Bagdasaryan Eugene","year":"2019","unstructured":"Eugene Bagdasaryan , Omid Poursaeed , and Vitaly Shmatikov . 2019. Differential Privacy Has Disparate Impact on Model Accuracy . In Advances in Neural Information Processing Systems, H.\u00a0Wallach, H.\u00a0Larochelle, A.\u00a0Beygelzimer, F.\u00a0d'Alch\u00e9-Buc, E.\u00a0Fox, and R.\u00a0Garnett (Eds.). Vol.\u00a032. Curran Associates , Inc .https:\/\/proceedings.neurips.cc\/paper\/ 2019 \/file\/fc0de4e0396fff257ea362983c2dda5a-Paper.pdf Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov. 2019. Differential Privacy Has Disparate Impact on Model Accuracy. In Advances in Neural Information Processing Systems, H.\u00a0Wallach, H.\u00a0Larochelle, A.\u00a0Beygelzimer, F.\u00a0d'Alch\u00e9-Buc, E.\u00a0Fox, and R.\u00a0Garnett (Eds.). Vol.\u00a032. Curran Associates, Inc.https:\/\/proceedings.neurips.cc\/paper\/2019\/file\/fc0de4e0396fff257ea362983c2dda5a-Paper.pdf"},{"key":"e_1_3_2_1_5_1","volume-title":"R\u00e9nyi Fair Inference. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=HkgsUJrtDB","author":"Baharlouei Sina","year":"2020","unstructured":"Sina Baharlouei , Maher Nouiehed , Ahmad Beirami , and Meisam Razaviyayn . 2020 . R\u00e9nyi Fair Inference. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=HkgsUJrtDB Sina Baharlouei, Maher Nouiehed, Ahmad Beirami, and Meisam Razaviyayn. 2020. R\u00e9nyi Fair Inference. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=HkgsUJrtDB"},{"key":"e_1_3_2_1_6_1","unstructured":"Michelle Bao Angela Zhou Samantha Zottola Brian Brubach Sarah Desmarais Aaron Horowitz Kristian Lum and Suresh Venkatasubramanian. 2021. It\u2019s COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks. arXiv preprint arXiv:2106.05498(2021).  Michelle Bao Angela Zhou Samantha Zottola Brian Brubach Sarah Desmarais Aaron Horowitz Kristian Lum and Suresh Venkatasubramanian. 2021. It\u2019s COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks. arXiv preprint arXiv:2106.05498(2021)."},{"key":"e_1_3_2_1_7_1","unstructured":"Matias Barenstein. 2019. ProPublica\u2019s COMPAS Data Revisited. arXiv preprint arXiv:1906.04711(2019).  Matias Barenstein. 2019. ProPublica\u2019s COMPAS Data Revisited. arXiv preprint arXiv:1906.04711(2019)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00041"},{"key":"e_1_3_2_1_9_1","volume-title":"On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?(FAccT \u201921)","author":"Bender M.","unstructured":"Emily\u00a0 M. Bender , Timnit Gebru , Angelina McMillan-Major , and Shmargaret Shmitchell . 2021. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?(FAccT \u201921) . Association for Computing Machinery , New York, NY, USA , 610\u2013623. https:\/\/doi.org\/10.1145\/3442188.3445922 10.1145\/3442188.3445922 Emily\u00a0M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?(FAccT \u201921). Association for Computing Machinery, New York, NY, USA, 610\u2013623. https:\/\/doi.org\/10.1145\/3442188.3445922"},{"key":"e_1_3_2_1_10_1","unstructured":"Richard Berk Hoda Heidari Shahin Jabbari Matthew Joseph Michael Kearns Jamie Morgenstern Seth Neel and Aaron Roth. 2017. A Convex Framework for Fair Regression. arxiv:cs.LG\/1706.02409KDD 2017 workshop: \u201cFairness Accountability and Transparency in Machine Learning (FAT\/ML)\u201d.  Richard Berk Hoda Heidari Shahin Jabbari Matthew Joseph Michael Kearns Jamie Morgenstern Seth Neel and Aaron Roth. 2017. A Convex Framework for Fair Regression. arxiv:cs.LG\/1706.02409KDD 2017 workshop: \u201cFairness Accountability and Transparency in Machine Learning (FAT\/ML)\u201d."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-010-0190-x"},{"key":"e_1_3_2_1_12_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning(Proceedings of Machine Learning Research), Jennifer Dy and Andreas Krause (Eds.). Vol.\u00a080","author":"Celis Elisa","year":"2018","unstructured":"Elisa Celis , Vijay Keswani , Damian Straszak , Amit Deshpande , Tarun Kathuria , and Nisheeth Vishnoi . 2018 . Fair and Diverse DPP-Based Data Summarization . In Proceedings of the 35th International Conference on Machine Learning(Proceedings of Machine Learning Research), Jennifer Dy and Andreas Krause (Eds.). Vol.\u00a080 . PMLR, Stockholmsm\u00e4ssan, Stockholm Sweden, 716\u2013725. http:\/\/proceedings.mlr.press\/v80\/celis18a.html Elisa Celis, Vijay Keswani, Damian Straszak, Amit Deshpande, Tarun Kathuria, and Nisheeth Vishnoi. 2018. Fair and Diverse DPP-Based Data Summarization. In Proceedings of the 35th International Conference on Machine Learning(Proceedings of Machine Learning Research), Jennifer Dy and Andreas Krause (Eds.). Vol.\u00a080. PMLR, Stockholmsm\u00e4ssan, Stockholm Sweden, 716\u2013725. http:\/\/proceedings.mlr.press\/v80\/celis18a.html"},{"key":"e_1_3_2_1_13_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). Vol.\u00a097","author":"Celis Elisa","year":"2019","unstructured":"Elisa Celis , Anay Mehrotra , and Nisheeth Vishnoi . 2019 . Toward Controlling Discrimination in Online Ad Auctions . In Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). Vol.\u00a097 . PMLR, Long Beach, California, USA, 4456\u20134465. http:\/\/proceedings.mlr.press\/v97\/mehrotra19a.html Elisa Celis, Anay Mehrotra, and Nisheeth Vishnoi. 2019. Toward Controlling Discrimination in Online Ad Auctions. In Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). Vol.\u00a097. PMLR, Long Beach, California, USA, 4456\u20134465. http:\/\/proceedings.mlr.press\/v97\/mehrotra19a.html"},{"key":"e_1_3_2_1_14_1","volume-title":"Advances in Neural Information Processing Systems, S.\u00a0Bengio, H.\u00a0Wallach, H.\u00a0Larochelle, K.\u00a0Grauman, N.\u00a0Cesa-Bianchi, and R.\u00a0Garnett (Eds.). Vol.\u00a031. Curran Associates","author":"Chen Binghui","year":"2018","unstructured":"Binghui Chen , Weihong Deng , and Haifeng Shen . 2018. Virtual Class Enhanced Discriminative Embedding Learning . In Advances in Neural Information Processing Systems, S.\u00a0Bengio, H.\u00a0Wallach, H.\u00a0Larochelle, K.\u00a0Grauman, N.\u00a0Cesa-Bianchi, and R.\u00a0Garnett (Eds.). Vol.\u00a031. Curran Associates , Inc .https:\/\/proceedings.neurips.cc\/paper\/ 2018 \/file\/d79aac075930c83c2f1e369a511148fe-Paper.pdf Binghui Chen, Weihong Deng, and Haifeng Shen. 2018. Virtual Class Enhanced Discriminative Embedding Learning. In Advances in Neural Information Processing Systems, S.\u00a0Bengio, H.\u00a0Wallach, H.\u00a0Larochelle, K.\u00a0Grauman, N.\u00a0Cesa-Bianchi, and R.\u00a0Garnett (Eds.). Vol.\u00a031. Curran Associates, Inc.https:\/\/proceedings.neurips.cc\/paper\/2018\/file\/d79aac075930c83c2f1e369a511148fe-Paper.pdf"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287594"},{"key":"e_1_3_2_1_16_1","volume-title":"Advances in Neural Information Processing Systems, I.\u00a0Guyon, U.\u00a0V. Luxburg, S.\u00a0Bengio, H.\u00a0Wallach, R.\u00a0Fergus, S.\u00a0Vishwanathan, and R.\u00a0Garnett (Eds.). Vol.\u00a030. Curran Associates","author":"Chierichetti Flavio","year":"2017","unstructured":"Flavio Chierichetti , Ravi Kumar , Silvio Lattanzi , and Sergei Vassilvitskii . 2017. Fair Clustering Through Fairlets . In Advances in Neural Information Processing Systems, I.\u00a0Guyon, U.\u00a0V. Luxburg, S.\u00a0Bengio, H.\u00a0Wallach, R.\u00a0Fergus, S.\u00a0Vishwanathan, and R.\u00a0Garnett (Eds.). Vol.\u00a030. Curran Associates , Inc ., 5029\u20135037. https:\/\/proceedings.neurips.cc\/paper\/ 2017 \/file\/978fce5bcc4eccc88ad48ce3914124a2-Paper.pdf Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, and Sergei Vassilvitskii. 2017. Fair Clustering Through Fairlets. In Advances in Neural Information Processing Systems, I.\u00a0Guyon, U.\u00a0V. Luxburg, S.\u00a0Bengio, H.\u00a0Wallach, R.\u00a0Fergus, S.\u00a0Vishwanathan, and R.\u00a0Garnett (Eds.). Vol.\u00a030. Curran Associates, Inc., 5029\u20135037. https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/978fce5bcc4eccc88ad48ce3914124a2-Paper.pdf"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372851"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3314236"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Kate Crawford and Trevor Paglen. 2021. Excavating AI: the Politics of Images in Machine Learning Training Sets. https:\/\/excavating.ai\/  Kate Crawford and Trevor Paglen. 2021. Excavating AI: the Politics of Images in Machine Learning Training Sets. https:\/\/excavating.ai\/","DOI":"10.1007\/s00146-021-01162-8"},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). Vol.\u00a097","author":"Creager Elliot","year":"2019","unstructured":"Elliot Creager , David Madras , Joern-Henrik Jacobsen , Marissa Weis , Kevin Swersky , Toniann Pitassi , and Richard Zemel . 2019 . Flexibly Fair Representation Learning by Disentanglement . In Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). Vol.\u00a097 . PMLR, Long Beach, California, USA, 1436\u20131445. http:\/\/proceedings.mlr.press\/v97\/creager19a.html Elliot Creager, David Madras, Joern-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, and Richard Zemel. 2019. Flexibly Fair Representation Learning by Disentanglement. In Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). Vol.\u00a097. PMLR, Long Beach, California, USA, 1436\u20131445. http:\/\/proceedings.mlr.press\/v97\/creager19a.html"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372878"},{"key":"e_1_3_2_1_22_1","volume-title":"ECAI","author":"Davidson Ian","year":"2020","unstructured":"Ian Davidson and Selvan\u00a0Suntiha Ravi . 2020 . A framework for determining the fairness of outlier detection . In ECAI 2020. IOS Press, 2465\u20132472. Ian Davidson and Selvan\u00a0Suntiha Ravi. 2020. A framework for determining the fairness of outlier detection. In ECAI 2020. IOS Press, 2465\u20132472."},{"key":"e_1_3_2_1_23_1","unstructured":"William Dieterich Christina Mendoza and Tim Brennan. 2016. COMPAS risk scales: Demonstrating accuracy equity and predictive parity.  William Dieterich Christina Mendoza and Tim Brennan. 2016. COMPAS risk scales: Demonstrating accuracy equity and predictive parity."},{"key":"e_1_3_2_1_24_1","volume-title":"Retiring adult: New datasets for fair machine learning. Advances in Neural Information Processing Systems 34","author":"Ding Frances","year":"2021","unstructured":"Frances Ding , Moritz Hardt , John Miller , and Ludwig Schmidt . 2021. Retiring adult: New datasets for fair machine learning. Advances in Neural Information Processing Systems 34 ( 2021 ). Frances Ding, Moritz Hardt, John Miller, and Ludwig Schmidt. 2021. Retiring adult: New datasets for fair machine learning. Advances in Neural Information Processing Systems 34 (2021)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090255"},{"key":"e_1_3_2_1_26_1","unstructured":"Simone Fabbrizzi Symeon Papadopoulos Eirini Ntoutsi and Ioannis Kompatsiaris. 2021. A survey on bias in visual datasets. arXiv preprint arXiv:2107.07919(2021).  Simone Fabbrizzi Symeon Papadopoulos Eirini Ntoutsi and Ioannis Kompatsiaris. 2021. A survey on bias in visual datasets. arXiv preprint arXiv:2107.07919(2021)."},{"key":"#cr-split#-e_1_3_2_1_27_1.1","doi-asserted-by":"crossref","unstructured":"Alessandro Fabris Stefano Messina Gianmaria Silvello and Gian\u00a0Antonio Susto. 2022. Algorithmic Fairness Datasets: the Story so Far. Data Mining and Knowledge Discovery(2022). https:\/\/doi.org\/10.1007\/s10618-022-00854-z to appear. 10.1007\/s10618-022-00854-z","DOI":"10.1007\/s10618-022-00854-z"},{"key":"#cr-split#-e_1_3_2_1_27_1.2","doi-asserted-by":"crossref","unstructured":"Alessandro Fabris Stefano Messina Gianmaria Silvello and Gian\u00a0Antonio Susto. 2022. Algorithmic Fairness Datasets: the Story so Far. Data Mining and Knowledge Discovery(2022). https:\/\/doi.org\/10.1007\/s10618-022-00854-z to appear.","DOI":"10.1007\/s10618-022-00854-z"},{"key":"e_1_3_2_1_28_1","volume-title":"Algorithmic Audit of Italian Car Insurance: Evidence of Unfairness in Access and Pricing","author":"Fabris Alessandro","unstructured":"Alessandro Fabris , Alan Mishler , Stefano Gottardi , Mattia Carletti , Matteo Daicampi , Gian\u00a0Antonio Susto , and Gianmaria Silvello . 2021. Algorithmic Audit of Italian Car Insurance: Evidence of Unfairness in Access and Pricing . Association for Computing Machinery , New York, NY, USA , 458\u2013468. https:\/\/doi.org\/10.1145\/3461702.3462569 10.1145\/3461702.3462569 Alessandro Fabris, Alan Mishler, Stefano Gottardi, Mattia Carletti, Matteo Daicampi, Gian\u00a0Antonio Susto, and Gianmaria Silvello. 2021. Algorithmic Audit of Italian Car Insurance: Evidence of Unfairness in Access and Pricing. Association for Computing Machinery, New York, NY, USA, 458\u2013468. https:\/\/doi.org\/10.1145\/3461702.3462569"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366424.3383555"},{"key":"e_1_3_2_1_30_1","volume-title":"Learning to Generate Fair Clusters from Demonstrations","author":"Galhotra Sainyam","unstructured":"Sainyam Galhotra , Sandhya Saisubramanian , and Shlomo Zilberstein . 2021. Learning to Generate Fair Clusters from Demonstrations . Association for Computing Machinery , New York, NY, USA , 491\u2013501. https:\/\/doi.org\/10.1145\/3461702.3462558 10.1145\/3461702.3462558 Sainyam Galhotra, Sandhya Saisubramanian, and Shlomo Zilberstein. 2021. Learning to Generate Fair Clusters from Demonstrations. Association for Computing Machinery, New York, NY, USA, 491\u2013501. https:\/\/doi.org\/10.1145\/3461702.3462558"},{"key":"e_1_3_2_1_31_1","unstructured":"Timnit Gebru Jamie Morgenstern Briana Vecchione Jennifer\u00a0Wortman Vaughan Hanna Wallach Hal Daum\u00e9\u00a0III and Kate Crawford. 2018. Datasheets for datasets. arXiv preprint arXiv:1803.09010(2018).  Timnit Gebru Jamie Morgenstern Briana Vecchione Jennifer\u00a0Wortman Vaughan Hanna Wallach Hal Daum\u00e9\u00a0III and Kate Crawford. 2018. Datasheets for datasets. arXiv preprint arXiv:1803.09010(2018)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372862"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3314282"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3375627.3375864"},{"key":"e_1_3_2_1_36_1","unstructured":"Sarah Holland Ahmed Hosny Sarah Newman Joshua Joseph and Kasia Chmielinski. 2018. The dataset nutrition label: A framework to drive higher data quality standards. arXiv preprint arXiv:1805.03677(2018).  Sarah Holland Ahmed Hosny Sarah Newman Joshua Joseph and Kasia Chmielinski. 2018. The dataset nutrition label: A framework to drive higher data quality standards. arXiv preprint arXiv:1805.03677(2018)."},{"key":"e_1_3_2_1_37_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). Vol.\u00a097","author":"Huang Lingxiao","year":"2019","unstructured":"Lingxiao Huang and Nisheeth Vishnoi . 2019 . Stable and Fair Classification . In Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). Vol.\u00a097 . PMLR, Long Beach, California, USA, 2879\u20132890. http:\/\/proceedings.mlr.press\/v97\/huang19e.html Lingxiao Huang and Nisheeth Vishnoi. 2019. Stable and Fair Classification. In Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). Vol.\u00a097. PMLR, Long Beach, California, USA, 2879\u20132890. http:\/\/proceedings.mlr.press\/v97\/huang19e.html"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3375627.3375848"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445918"},{"key":"e_1_3_2_1_40_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). Vol.\u00a097","author":"Jagielski Matthew","year":"2019","unstructured":"Matthew Jagielski , Michael Kearns , Jieming Mao , Alina Oprea , Aaron Roth , Saeed\u00a0Sharifi Malvajerdi , and Jonathan Ullman . 2019 . Differentially Private Fair Learning . In Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). Vol.\u00a097 . PMLR, Long Beach, California, USA, 3000\u20133008. http:\/\/proceedings.mlr.press\/v97\/jagielski19a.html Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed\u00a0Sharifi Malvajerdi, and Jonathan Ullman. 2019. Differentially Private Fair Learning. In Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). Vol.\u00a097. PMLR, Long Beach, California, USA, 3000\u20133008. http:\/\/proceedings.mlr.press\/v97\/jagielski19a.html"},{"key":"e_1_3_2_1_41_1","volume-title":"Assessing Fairness with Unlabeled Data and Bayesian Inference. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020","author":"Ji Disi","year":"2020","unstructured":"Disi Ji , Padhraic Smyth , and Mark Steyvers . 2020 . Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020 , NeurIPS 2020, December 6-12, 2020, virtual, Hugo Larochelle, Marc\u2019Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/d83de59e10227072a9c034ce10029c39-Abstract.html Disi Ji, Padhraic Smyth, and Mark Steyvers. 2020. Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, Hugo Larochelle, Marc\u2019Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/d83de59e10227072a9c034ce10029c39-Abstract.html"},{"key":"e_1_3_2_1_42_1","volume-title":"Selective Classification Can Magnify Disparities Across Groups. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=N0M_4BkQ05i","author":"Jones Erik","year":"2021","unstructured":"Erik Jones , Shiori Sagawa , Pang\u00a0Wei Koh , Ananya Kumar , and Percy Liang . 2021 . Selective Classification Can Magnify Disparities Across Groups. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=N0M_4BkQ05i Erik Jones, Shiori Sagawa, Pang\u00a0Wei Koh, Ananya Kumar, and Percy Liang. 2021. Selective Classification Can Magnify Disparities Across Groups. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=N0M_4BkQ05i"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445895"},{"key":"e_1_3_2_1_44_1","volume-title":"InFoRM: Individual Fairness on Graph Mining","author":"Kang Jian","unstructured":"Jian Kang , Jingrui He , Ross Maciejewski , and Hanghang Tong . 2020. InFoRM: Individual Fairness on Graph Mining . Association for Computing Machinery , New York, NY, USA , 379\u2013389. https:\/\/doi.org\/10.1145\/3394486.3403080 10.1145\/3394486.3403080 Jian Kang, Jingrui He, Ross Maciejewski, and Hanghang Tong. 2020. InFoRM: Individual Fairness on Graph Mining. Association for Computing Machinery, New York, NY, USA, 379\u2013389. https:\/\/doi.org\/10.1145\/3394486.3403080"},{"key":"e_1_3_2_1_45_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rJzLciCqKm","author":"Kato Masahiro","year":"2019","unstructured":"Masahiro Kato , Takeshi Teshima , and Junya Honda . 2019 . Learning from Positive and Unlabeled Data with a Selection Bias . In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rJzLciCqKm Masahiro Kato, Takeshi Teshima, and Junya Honda. 2019. Learning from Positive and Unlabeled Data with a Selection Bias. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rJzLciCqKm"},{"key":"e_1_3_2_1_46_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning(Proceedings of Machine Learning Research), Jennifer Dy and Andreas Krause (Eds.). Vol.\u00a080","author":"Kearns Michael","year":"2018","unstructured":"Michael Kearns , Seth Neel , Aaron Roth , and Zhiwei\u00a0Steven Wu . 2018 . Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness . In Proceedings of the 35th International Conference on Machine Learning(Proceedings of Machine Learning Research), Jennifer Dy and Andreas Krause (Eds.). Vol.\u00a080 . PMLR, Stockholmsm\u00e4ssan, Stockholm Sweden, 2564\u20132572. http:\/\/proceedings.mlr.press\/v80\/kearns18a.html Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei\u00a0Steven Wu. 2018. Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. In Proceedings of the 35th International Conference on Machine Learning(Proceedings of Machine Learning Research), Jennifer Dy and Andreas Krause (Eds.). Vol.\u00a080. PMLR, Stockholmsm\u00e4ssan, Stockholm Sweden, 2564\u20132572. http:\/\/proceedings.mlr.press\/v80\/kearns18a.html"},{"key":"e_1_3_2_1_47_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning(Proceedings of Machine Learning Research), Jennifer Dy and Andreas Krause (Eds.). Vol.\u00a080","author":"Kilbertus Niki","year":"2018","unstructured":"Niki Kilbertus , Adria Gascon , Matt Kusner , Michael Veale , Krishna Gummadi , and Adrian Weller . 2018 . Blind Justice: Fairness with Encrypted Sensitive Attributes . In Proceedings of the 35th International Conference on Machine Learning(Proceedings of Machine Learning Research), Jennifer Dy and Andreas Krause (Eds.). Vol.\u00a080 . PMLR, Stockholmsm\u00e4ssan, Stockholm Sweden, 2630\u20132639. http:\/\/proceedings.mlr.press\/v80\/kilbertus18a.html Niki Kilbertus, Adria Gascon, Matt Kusner, Michael Veale, Krishna Gummadi, and Adrian Weller. 2018. Blind Justice: Fairness with Encrypted Sensitive Attributes. In Proceedings of the 35th International Conference on Machine Learning(Proceedings of Machine Learning Research), Jennifer Dy and Andreas Krause (Eds.). Vol.\u00a080. PMLR, Stockholmsm\u00e4ssan, Stockholm Sweden, 2630\u20132639. http:\/\/proceedings.mlr.press\/v80\/kilbertus18a.html"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330899"},{"key":"e_1_3_2_1_49_1","volume-title":"Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2). https:\/\/openreview.net\/forum?id=zNQBIBKJRkd","author":"Koch Bernard","year":"2021","unstructured":"Bernard Koch , Emily Denton , Alex Hanna , and Jacob\u00a0Gates Foster . 2021 . Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research . In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2). https:\/\/openreview.net\/forum?id=zNQBIBKJRkd Bernard Koch, Emily Denton, Alex Hanna, and Jacob\u00a0Gates Foster. 2021. Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2). https:\/\/openreview.net\/forum?id=zNQBIBKJRkd"},{"key":"e_1_3_2_1_50_1","volume-title":"Advances in Neural Information Processing Systems, H.\u00a0Wallach, H.\u00a0Larochelle, A.\u00a0Beygelzimer, F.\u00a0d'Alch\u00e9-Buc, E.\u00a0Fox, and R.\u00a0Garnett (Eds.). Vol.\u00a032. Curran Associates","author":"Lamy Alex","year":"2019","unstructured":"Alex Lamy , Ziyuan Zhong , Aditya\u00a0 K Menon , and Nakul Verma . 2019. Noise-tolerant fair classification . In Advances in Neural Information Processing Systems, H.\u00a0Wallach, H.\u00a0Larochelle, A.\u00a0Beygelzimer, F.\u00a0d'Alch\u00e9-Buc, E.\u00a0Fox, and R.\u00a0Garnett (Eds.). Vol.\u00a032. Curran Associates , Inc ., 294\u2013306. https:\/\/proceedings.neurips.cc\/paper\/ 2019 \/file\/8d5e957f297893487bd98fa830fa6413-Paper.pdf Alex Lamy, Ziyuan Zhong, Aditya\u00a0K Menon, and Nakul Verma. 2019. Noise-tolerant fair classification. In Advances in Neural Information Processing Systems, H.\u00a0Wallach, H.\u00a0Larochelle, A.\u00a0Beygelzimer, F.\u00a0d'Alch\u00e9-Buc, E.\u00a0Fox, and R.\u00a0Garnett (Eds.). Vol.\u00a032. Curran Associates, Inc., 294\u2013306. https:\/\/proceedings.neurips.cc\/paper\/2019\/file\/8d5e957f297893487bd98fa830fa6413-Paper.pdf"},{"key":"e_1_3_2_1_51_1","unstructured":"Jeff Larson Surya Mattu Lauren Kirchner and Julia Angwin. 2016. How We Analyzed the COMPAS Recidivism Algorithm. https:\/\/www.propublica.org\/article\/how-we-analyzed-the-compas-recidivism-algorithm  Jeff Larson Surya Mattu Lauren Kirchner and Julia Angwin. 2016. How We Analyzed the COMPAS Recidivism Algorithm. https:\/\/www.propublica.org\/article\/how-we-analyzed-the-compas-recidivism-algorithm"},{"key":"e_1_3_2_1_52_1","volume-title":"A survey on datasets for fairness-aware machine learning. WIREs Data Mining and Knowledge Discovery n\/a, n\/a","author":"Le\u00a0Quy Tai","year":"2022","unstructured":"Tai Le\u00a0Quy , Arjun Roy , Vasileios Iosifidis , Wenbin Zhang , and Eirini Ntoutsi . 2022. A survey on datasets for fairness-aware machine learning. WIREs Data Mining and Knowledge Discovery n\/a, n\/a ( 2022 ), e1452. https:\/\/doi.org\/10.1002\/widm.1452 arXiv:https:\/\/wires.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/widm.1452 10.1002\/widm.1452 Tai Le\u00a0Quy, Arjun Roy, Vasileios Iosifidis, Wenbin Zhang, and Eirini Ntoutsi. 2022. A survey on datasets for fairness-aware machine learning. WIREs Data Mining and Knowledge Discovery n\/a, n\/a (2022), e1452. https:\/\/doi.org\/10.1002\/widm.1452 arXiv:https:\/\/wires.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/widm.1452"},{"key":"e_1_3_2_1_53_1","volume-title":"Fair Resource Allocation in Federated Learning. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ByexElSYDr","author":"Li Tian","year":"2020","unstructured":"Tian Li , Maziar Sanjabi , Ahmad Beirami , and Virginia Smith . 2020 . Fair Resource Allocation in Federated Learning. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ByexElSYDr Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith. 2020. Fair Resource Allocation in Federated Learning. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ByexElSYDr"},{"key":"e_1_3_2_1_54_1","volume-title":"Towards Fair Truth Discovery from Biased Crowdsourced Answers","author":"Li Yanying","unstructured":"Yanying Li , Haipei Sun , and Wendy\u00a0Hui Wang . 2020. Towards Fair Truth Discovery from Biased Crowdsourced Answers . Association for Computing Machinery , New York, NY, USA , 599\u2013607. https:\/\/doi.org\/10.1145\/3394486.3403102 10.1145\/3394486.3403102 Yanying Li, Haipei Sun, and Wendy\u00a0Hui Wang. 2020. Towards Fair Truth Discovery from Biased Crowdsourced Answers. Association for Computing Machinery, New York, NY, USA, 599\u2013607. https:\/\/doi.org\/10.1145\/3394486.3403102"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220014"},{"key":"e_1_3_2_1_56_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning(Proceedings of Machine Learning Research), Jennifer Dy and Andreas Krause (Eds.). Vol.\u00a080","author":"Liu T.","year":"2018","unstructured":"Lydia\u00a0 T. Liu , Sarah Dean , Esther Rolf , Max Simchowitz , and Moritz Hardt . 2018 . Delayed Impact of Fair Machine Learning . In Proceedings of the 35th International Conference on Machine Learning(Proceedings of Machine Learning Research), Jennifer Dy and Andreas Krause (Eds.). Vol.\u00a080 . PMLR, Stockholmsm\u00e4ssan, Stockholm Sweden, 3150\u20133158. http:\/\/proceedings.mlr.press\/v80\/liu18c.html Lydia\u00a0T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, and Moritz Hardt. 2018. Delayed Impact of Fair Machine Learning. In Proceedings of the 35th International Conference on Machine Learning(Proceedings of Machine Learning Research), Jennifer Dy and Andreas Krause (Eds.). Vol.\u00a080. PMLR, Stockholmsm\u00e4ssan, Stockholm Sweden, 3150\u20133158. http:\/\/proceedings.mlr.press\/v80\/liu18c.html"},{"key":"e_1_3_2_1_57_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning(Proceedings of Machine Learning Research), Hal\u00a0Daum\u00e9 III and Aarti Singh (Eds.). Vol.\u00a0119","author":"Lohaus Michael","year":"2020","unstructured":"Michael Lohaus , Michael Perrot , and Ulrike\u00a0Von Luxburg . 2020 . Too Relaxed to Be Fair . In Proceedings of the 37th International Conference on Machine Learning(Proceedings of Machine Learning Research), Hal\u00a0Daum\u00e9 III and Aarti Singh (Eds.). Vol.\u00a0119 . PMLR, Virtual, 6360\u20136369. http:\/\/proceedings.mlr.press\/v119\/lohaus20a.html Michael Lohaus, Michael Perrot, and Ulrike\u00a0Von Luxburg. 2020. Too Relaxed to Be Fair. In Proceedings of the 37th International Conference on Machine Learning(Proceedings of Machine Learning Research), Hal\u00a0Daum\u00e9 III and Aarti Singh (Eds.). Vol.\u00a0119. PMLR, Virtual, 6360\u20136369. http:\/\/proceedings.mlr.press\/v119\/lohaus20a.html"},{"key":"e_1_3_2_1_58_1","unstructured":"David Madras Toni Pitassi and Richard Zemel. 2018. Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer. In Advances in Neural Information Processing Systems S.\u00a0Bengio H.\u00a0Wallach H.\u00a0Larochelle K.\u00a0Grauman N.\u00a0Cesa-Bianchi and R.\u00a0Garnett (Eds.). Vol.\u00a031. Curran Associates Inc. 6147\u20136157. https:\/\/proceedings.neurips.cc\/paper\/2018\/file\/09d37c08f7b129e96277388757530c72-Paper.pdf  David Madras Toni Pitassi and Richard Zemel. 2018. Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer. In Advances in Neural Information Processing Systems S.\u00a0Bengio H.\u00a0Wallach H.\u00a0Larochelle K.\u00a0Grauman N.\u00a0Cesa-Bianchi and R.\u00a0Garnett (Eds.). Vol.\u00a031. Curran Associates Inc. 6147\u20136157. https:\/\/proceedings.neurips.cc\/paper\/2018\/file\/09d37c08f7b129e96277388757530c72-Paper.pdf"},{"key":"e_1_3_2_1_59_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning(Proceedings of Machine Learning Research), Hal\u00a0Daum\u00e9 III and Aarti Singh (Eds.). Vol.\u00a0119","author":"Martinez Natalia","year":"2020","unstructured":"Natalia Martinez , Martin Bertran , and Guillermo Sapiro . 2020 . Minimax Pareto Fairness: A Multi Objective Perspective . In Proceedings of the 37th International Conference on Machine Learning(Proceedings of Machine Learning Research), Hal\u00a0Daum\u00e9 III and Aarti Singh (Eds.). Vol.\u00a0119 . PMLR, Virtual, 6755\u20136764. http:\/\/proceedings.mlr.press\/v119\/martinez20a.html Natalia Martinez, Martin Bertran, and Guillermo Sapiro. 2020. Minimax Pareto Fairness: A Multi Objective Perspective. In Proceedings of the 37th International Conference on Machine Learning(Proceedings of Machine Learning Research), Hal\u00a0Daum\u00e9 III and Aarti Singh (Eds.). Vol.\u00a0119. PMLR, Virtual, 6755\u20136764. http:\/\/proceedings.mlr.press\/v119\/martinez20a.html"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445902"},{"key":"e_1_3_2_1_61_1","first-page":"331","article-title":"Income measurement error in surveys: A review","volume":"16","author":"Moore C","year":"2000","unstructured":"Jeffrey\u00a0 C Moore , Linda\u00a0 L Stinson , and Edward\u00a0 J Welniak . 2000 . Income measurement error in surveys: A review . Journal of Official Statistics-Stockholm- 16 , 4 (2000), 331 \u2013 362 . Jeffrey\u00a0C Moore, Linda\u00a0L Stinson, and Edward\u00a0J Welniak. 2000. Income measurement error in surveys: A review. Journal of Official Statistics-Stockholm- 16, 4 (2000), 331\u2013362.","journal-title":"Journal of Official Statistics-Stockholm-"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445910"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3314277"},{"key":"e_1_3_2_1_64_1","unstructured":"Partnership on AI. 2022. About ML. Technical Report. https:\/\/partnershiponai.org\/workstream\/about-ml\/  Partnership on AI. 2022. About ML. Technical Report. https:\/\/partnershiponai.org\/workstream\/about-ml\/"},{"key":"e_1_3_2_1_65_1","unstructured":"Amandalynne Paullada Inioluwa\u00a0Deborah Raji Emily\u00a0M Bender Emily Denton and Alex Hanna. 2020. Data and its (dis) contents: A survey of dataset development and use in machine learning research. arXiv preprint arXiv:2012.05345(2020).  Amandalynne Paullada Inioluwa\u00a0Deborah Raji Emily\u00a0M Bender Emily Denton and Alex Hanna. 2020. Data and its (dis) contents: A survey of dataset development and use in machine learning research. arXiv preprint arXiv:2012.05345(2020)."},{"key":"e_1_3_2_1_66_1","unstructured":"Kenny Peng Arunesh Mathur and Arvind Narayanan. 2021. Mitigating dataset harms requires stewardship: Lessons from 1000 papers. arXiv preprint arXiv:2108.02922(2021).  Kenny Peng Arunesh Mathur and Arvind Narayanan. 2021. Mitigating dataset harms requires stewardship: Lessons from 1000 papers. arXiv preprint arXiv:2108.02922(2021)."},{"key":"e_1_3_2_1_67_1","volume-title":"Fair Bayesian Optimization","author":"Perrone Valerio","unstructured":"Valerio Perrone , Michele Donini , Muhammad\u00a0Bilal Zafar , Robin Schmucker , Krishnaram Kenthapadi , and C\u00e9dric Archambeau . 2021. Fair Bayesian Optimization . Association for Computing Machinery , New York, NY, USA , 854\u2013863. https:\/\/doi.org\/10.1145\/3461702.3462629 10.1145\/3461702.3462629 Valerio Perrone, Michele Donini, Muhammad\u00a0Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, and C\u00e9dric Archambeau. 2021. Fair Bayesian Optimization. Association for Computing Machinery, New York, NY, USA, 854\u2013863. https:\/\/doi.org\/10.1145\/3461702.3462629"},{"key":"e_1_3_2_1_68_1","unstructured":"ProPublica. 2016. COMPAS analysis github repository. https:\/\/github.com\/propublica\/compas-analysis  ProPublica. 2016. COMPAS analysis github repository. https:\/\/github.com\/propublica\/compas-analysis"},{"key":"e_1_3_2_1_69_1","volume-title":"GAEA: Graph Augmentation for Equitable Access via Reinforcement Learning","author":"Ramachandran Govardana\u00a0Sachithanandam","year":"2021","unstructured":"Govardana\u00a0Sachithanandam Ramachandran , Ivan Brugere , Lav\u00a0 R. Varshney , and Caiming Xiong . 2021 . GAEA: Graph Augmentation for Equitable Access via Reinforcement Learning . Association for Computing Machinery , New York, NY, USA , 884\u2013894. https:\/\/doi.org\/10.1145\/3461702.3462615 10.1145\/3461702.3462615 Govardana\u00a0Sachithanandam Ramachandran, Ivan Brugere, Lav\u00a0R. Varshney, and Caiming Xiong. 2021. GAEA: Graph Augmentation for Equitable Access via Reinforcement Learning. Association for Computing Machinery, New York, NY, USA, 884\u2013894. https:\/\/doi.org\/10.1145\/3461702.3462615"},{"key":"e_1_3_2_1_70_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning(Proceedings of Machine Learning Research), Hal\u00a0Daum\u00e9 III and Aarti Singh (Eds.). Vol.\u00a0119","author":"Sabato Sivan","year":"2020","unstructured":"Sivan Sabato and Elad Yom-Tov . 2020 . Bounding the fairness and accuracy of classifiers from population statistics . In Proceedings of the 37th International Conference on Machine Learning(Proceedings of Machine Learning Research), Hal\u00a0Daum\u00e9 III and Aarti Singh (Eds.). Vol.\u00a0119 . PMLR, Virtual, 8316\u20138325. http:\/\/proceedings.mlr.press\/v119\/sabato20a.html Sivan Sabato and Elad Yom-Tov. 2020. Bounding the fairness and accuracy of classifiers from population statistics. In Proceedings of the 37th International Conference on Machine Learning(Proceedings of Machine Learning Research), Hal\u00a0Daum\u00e9 III and Aarti Singh (Eds.). Vol.\u00a0119. PMLR, Virtual, 8316\u20138325. http:\/\/proceedings.mlr.press\/v119\/sabato20a.html"},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1145\/3476058"},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/3392866"},{"key":"e_1_3_2_1_73_1","volume-title":"FaiR-N: Fair and Robust Neural Networks for Structured Data","author":"Sharma Shubham","unstructured":"Shubham Sharma , Alan\u00a0 H. Gee , David Paydarfar , and Joydeep Ghosh . 2021. FaiR-N: Fair and Robust Neural Networks for Structured Data . Association for Computing Machinery , New York, NY, USA , 946\u2013955. https:\/\/doi.org\/10.1145\/3461702.3462559 10.1145\/3461702.3462559 Shubham Sharma, Alan\u00a0H. Gee, David Paydarfar, and Joydeep Ghosh. 2021. FaiR-N: Fair and Robust Neural Networks for Structured Data. Association for Computing Machinery, New York, NY, USA, 946\u2013955. https:\/\/doi.org\/10.1145\/3461702.3462559"},{"key":"e_1_3_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445865"},{"key":"e_1_3_2_1_75_1","unstructured":"UCI Machine Learning Repository. 1994. Statlog (German Credit Data) Data Set. https:\/\/archive.ics.uci.edu\/ml\/datasets\/statlog+(german+credit+data)  UCI Machine Learning Repository. 1994. Statlog (German Credit Data) Data Set. https:\/\/archive.ics.uci.edu\/ml\/datasets\/statlog+(german+credit+data)"},{"key":"e_1_3_2_1_76_1","unstructured":"UCI Machine Learning Repository. 2019. South German Credit Data Set. https:\/\/archive.ics.uci.edu\/ml\/datasets\/South+German+Credit  UCI Machine Learning Repository. 2019. South German Credit Data Set. https:\/\/archive.ics.uci.edu\/ml\/datasets\/South+German+Credit"},{"key":"e_1_3_2_1_77_1","volume-title":"of Commerce Bureau of the Census","author":"Dept US","year":"1995","unstructured":"US Dept . of Commerce Bureau of the Census . 1995 . Current Population Survey: Annual Demographic File , 1994. US Dept. of Commerce Bureau of the Census. 1995. Current Population Survey: Annual Demographic File, 1994."},{"key":"e_1_3_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445915"},{"key":"e_1_3_2_1_79_1","volume-title":"Fairness with Overlapping Groups","author":"Yang Forest","year":"2020","unstructured":"Forest Yang , Mouhamadou Cisse , and Sanmi Koyejo . 2020. Fairness with Overlapping Groups ; a Probabilistic Perspective. In Advances in Neural Information Processing Systems, H.\u00a0Larochelle, M.\u00a0Ranzato, R.\u00a0Hadsell, M.\u00a0F. Balcan, and H.\u00a0Lin (Eds.). Vol.\u00a033. Curran Associates, Inc ., 4067\u20134078. https:\/\/proceedings.neurips.cc\/paper\/ 2020 \/file\/29c0605a3bab4229e46723f89cf59d83-Paper.pdf Forest Yang, Mouhamadou Cisse, and Sanmi Koyejo. 2020. Fairness with Overlapping Groups; a Probabilistic Perspective. In Advances in Neural Information Processing Systems, H.\u00a0Larochelle, M.\u00a0Ranzato, R.\u00a0Hadsell, M.\u00a0F. Balcan, and H.\u00a0Lin (Eds.). Vol.\u00a033. Curran Associates, Inc., 4067\u20134078. https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/29c0605a3bab4229e46723f89cf59d83-Paper.pdf"},{"key":"e_1_3_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1145\/3085504.3085526"},{"key":"e_1_3_2_1_81_1","volume-title":"Advances in Neural Information Processing Systems, I.\u00a0Guyon, U.\u00a0V. Luxburg, S.\u00a0Bengio, H.\u00a0Wallach, R.\u00a0Fergus, S.\u00a0Vishwanathan, and R.\u00a0Garnett (Eds.). Vol.\u00a030. Curran Associates","author":"Zafar Muhammad\u00a0Bilal","year":"2017","unstructured":"Muhammad\u00a0Bilal Zafar , Isabel Valera , Manuel Rodriguez , Krishna Gummadi , and Adrian Weller . 2017. From Parity to Preference-based Notions of Fairness in Classification . In Advances in Neural Information Processing Systems, I.\u00a0Guyon, U.\u00a0V. Luxburg, S.\u00a0Bengio, H.\u00a0Wallach, R.\u00a0Fergus, S.\u00a0Vishwanathan, and R.\u00a0Garnett (Eds.). Vol.\u00a030. Curran Associates , Inc ., 229\u2013239. https:\/\/proceedings.neurips.cc\/paper\/ 2017 \/file\/82161242827b703e6acf9c726942a1e4-Paper.pdf Muhammad\u00a0Bilal Zafar, Isabel Valera, Manuel Rodriguez, Krishna Gummadi, and Adrian Weller. 2017. From Parity to Preference-based Notions of Fairness in Classification. In Advances in Neural Information Processing Systems, I.\u00a0Guyon, U.\u00a0V. Luxburg, S.\u00a0Bengio, H.\u00a0Wallach, R.\u00a0Fergus, S.\u00a0Vishwanathan, and R.\u00a0Garnett (Eds.). Vol.\u00a030. Curran Associates, Inc., 229\u2013239. https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/82161242827b703e6acf9c726942a1e4-Paper.pdf"},{"key":"e_1_3_2_1_82_1","volume-title":"Fairness Constraints: Mechanisms for Fair classification. In Artificial Intelligence and Statistics. PMLR, 962\u2013970.","author":"Zafar Muhammad\u00a0Bilal","year":"2017","unstructured":"Muhammad\u00a0Bilal Zafar , Isabel Valera , Manuel\u00a0Gomez Rogriguez , and Krishna\u00a0 P Gummadi . 2017 . Fairness Constraints: Mechanisms for Fair classification. In Artificial Intelligence and Statistics. PMLR, 962\u2013970. Muhammad\u00a0Bilal Zafar, Isabel Valera, Manuel\u00a0Gomez Rogriguez, and Krishna\u00a0P Gummadi. 2017. Fairness Constraints: Mechanisms for Fair classification. In Artificial Intelligence and Statistics. PMLR, 962\u2013970."},{"key":"e_1_3_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445878"},{"key":"e_1_3_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1145\/3375627.3375862"},{"key":"e_1_3_2_1_85_1","volume-title":"Maintaining Discrimination and Fairness in Class Incremental Learning. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR).","author":"Zhao Bowen","year":"2020","unstructured":"Bowen Zhao , Xi Xiao , Guojun Gan , Bin Zhang , and Shu-Tao Xia . 2020 . Maintaining Discrimination and Fairness in Class Incremental Learning. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Bowen Zhao, Xi Xiao, Guojun Gan, Bin Zhang, and Shu-Tao Xia. 2020. Maintaining Discrimination and Fairness in Class Incremental Learning. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_3_2_1_86_1","volume-title":"Fair Meta-Learning For Few-Shot Classification. In 2020 IEEE International Conference on Knowledge Graph (ICKG). 275\u2013282","author":"Zhao Chen","year":"2020","unstructured":"Chen Zhao , Changbin Li , Jincheng Li , and Feng Chen . 2020 . Fair Meta-Learning For Few-Shot Classification. In 2020 IEEE International Conference on Knowledge Graph (ICKG). 275\u2013282 . https:\/\/doi.org\/10.1109\/ICBK50248.2020.00047 10.1109\/ICBK50248.2020.00047 Chen Zhao, Changbin Li, Jincheng Li, and Feng Chen. 2020. Fair Meta-Learning For Few-Shot Classification. In 2020 IEEE International Conference on Knowledge Graph (ICKG). 275\u2013282. https:\/\/doi.org\/10.1109\/ICBK50248.2020.00047"},{"key":"e_1_3_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01550"},{"key":"e_1_3_2_1_88_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00800"}],"event":{"name":"EAAMO '22: Equity and Access in Algorithms, Mechanisms, and Optimization","location":"Arlington VA USA","acronym":"EAAMO '22","sponsor":["SIGAI ACM Special Interest Group on Artificial Intelligence","SIGecom Special Interest Group on Economics and Computation"]},"container-title":["Equity and Access in Algorithms, Mechanisms, and Optimization"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3551624.3555286","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3551624.3555286","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:00:25Z","timestamp":1750186825000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3551624.3555286"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,6]]},"references-count":88,"alternative-id":["10.1145\/3551624.3555286","10.1145\/3551624"],"URL":"https:\/\/doi.org\/10.1145\/3551624.3555286","relation":{},"subject":[],"published":{"date-parts":[[2022,10,6]]},"assertion":[{"value":"2022-10-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}