{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T06:46:32Z","timestamp":1778049992336,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":55,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T00:00:00Z","timestamp":1614556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100014895","name":"Open Philanthropy Project","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100014895","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,3,3]]},"DOI":"10.1145\/3442188.3445883","type":"proceedings-article","created":{"date-parts":[[2021,3,3]],"date-time":"2021-03-03T01:26:24Z","timestamp":1614734784000},"page":"196-205","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Removing Spurious Features can Hurt Accuracy and Affect Groups Disproportionately"],"prefix":"10.1145","author":[{"given":"Fereshte","family":"Khani","sequence":"first","affiliation":[{"name":"Stanford University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Percy","family":"Liang","sequence":"additional","affiliation":[{"name":"Stanford University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,3]]},"reference":[{"key":"e_1_3_2_2_1_1","first-page":"60","volume-title":"International Conference on Machine Learning (ICML)","author":"Agarwal Alekh","year":"2018","unstructured":"Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, and Hanna Wallach. A reductions approach to fair classification. In International Conference on Machine Learning (ICML), pages 60--69, 2018."},{"key":"e_1_3_2_2_2_1","volume-title":"Benign overfitting in linear regression. arXiv","author":"Bartlett Peter L.","year":"2019","unstructured":"Peter L. Bartlett, Philip M. Long, Gabor Lugosi, and Alexander Tsigler. Benign overfitting in linear regression. arXiv, 2019."},{"key":"e_1_3_2_2_3_1","volume-title":"Two models of double descent for weak features. arXiv","author":"Belkin Mikhail","year":"2019","unstructured":"Mikhail Belkin, Daniel Hsu, and Ji Xu. Two models of double descent for weak features. arXiv, 2019."},{"key":"e_1_3_2_2_4_1","volume-title":"H Chi. Data decisions and theoretical implications when adversarially learning fair representations. arXiv preprint arXiv:1707.00075","author":"Beutel Alex","year":"2017","unstructured":"Alex Beutel, Jilin Chen, Zhe Zhao, and Ed H Chi. Data decisions and theoretical implications when adversarially learning fair representations. arXiv preprint arXiv:1707.00075, 2017."},{"key":"e_1_3_2_2_5_1","first-page":"4349","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","author":"Bolukbasi Tolga","year":"2016","unstructured":"Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In Advances in Neural Information Processing Systems (NeurIPS), pages 4349--4357, 2016."},{"key":"e_1_3_2_2_6_1","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","author":"Carmon Yair","year":"2019","unstructured":"Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, and John C. Duchi. Unlabeled data improves adversarial robustness. In Advances in Neural Information Processing Systems (NeurIPS), 2019."},{"key":"e_1_3_2_2_7_1","first-page":"7801","volume-title":"Path-specific counterfactual fairness","author":"Chiappa Silvia","year":"2019","unstructured":"Silvia Chiappa. Path-specific counterfactual fairness. In Association for the Advancement of Artificial Intelligence (AAAI), volume 33, pages 7801--7808, 2019."},{"key":"e_1_3_2_2_8_1","volume-title":"Flexibly fair representation learning by disentanglement. arXiv preprint arXiv:1906.02589","author":"Creager Elliot","year":"2019","unstructured":"Elliot Creager, David Madras, J\u00f6rn-Henrik Jacobsen, Marissa A Weis, Kevin Swersky, Toniann Pitassi, and Richard Zemel. Flexibly fair representation learning by disentanglement. arXiv preprint arXiv:1906.02589, 2019."},{"key":"e_1_3_2_2_9_1","first-page":"67","volume-title":"Measuring and mitigating unintended bias in text classification","author":"Dixon Lucas","year":"2018","unstructured":"Lucas Dixon, John Li, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman. Measuring and mitigating unintended bias in text classification. In Association for the Advancement of Artificial Intelligence (AAAI), pages 67--73, 2018."},{"key":"e_1_3_2_2_10_1","volume-title":"International Conference on Machine Learning (ICML)","author":"Dutta Sanghamitra","year":"2020","unstructured":"Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, and Kush R. Varshney. Is there a trade-off between fairness and accuracy? a perspective using mismatched hypothesis testing. In International Conference on Machine Learning (ICML), 2020."},{"key":"e_1_3_2_2_11_1","volume-title":"Moving object detection in spatial domain using background removal techniques-state-of-art. Recent patents on computer science, 1(1):32--54","author":"Elhabian Shireen Y","year":"2008","unstructured":"Shireen Y Elhabian, Khaled M El-Sayed, and Sumaya H Ahmed. Moving object detection in spatial domain using background removal techniques-state-of-art. Recent patents on computer science, 1(1):32--54, 2008."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-017-5663-3"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1177\/0193841X04266432"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1080\/03610926.2015.1100742"},{"key":"e_1_3_2_2_15_1","first-page":"219","volume-title":"H Chi, and Alex Beutel. Counterfactual fairness in text classification through robustness","author":"Garg Sahaj","year":"2019","unstructured":"Sahaj Garg, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H Chi, and Alex Beutel. Counterfactual fairness in text classification through robustness. In Association for the Advancement of Artificial Intelligence (AAAI), pages 219--226, 2019."},{"key":"e_1_3_2_2_16_1","volume-title":"International Conference on Learning Representations (ICLR)","author":"Goodfellow Ian J","year":"2015","unstructured":"Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. In International Conference on Learning Representations (ICLR), 2015."},{"key":"e_1_3_2_2_17_1","first-page":"6151","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","author":"Gunasekar Suriya","year":"2017","unstructured":"Suriya Gunasekar, Blake E Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, and Nati Srebro. Implicit regularization in matrix factorization. In Advances in Neural Information Processing Systems (NeurIPS), pages 6151--6159, 2017."},{"key":"e_1_3_2_2_18_1","first-page":"3315","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","author":"Hardt Moritz","year":"2016","unstructured":"Moritz Hardt, Eric Price, and Nathan Srebo. Equality of opportunity in supervised learning. In Advances in Neural Information Processing Systems (NeurIPS), pages 3315--3323, 2016."},{"key":"e_1_3_2_2_19_1","series-title":"ETS Research Report Series","volume-title":"Causation and race","author":"Holland Paul W","year":"2003","unstructured":"Paul W Holland. Causation and race. ETS Research Report Series, 2003(1), 2003."},{"key":"e_1_3_2_2_20_1","volume-title":"Adversarial examples are not bugs, they are features. arXiv preprint arXiv:1905.02175","author":"Ilyas Andrew","year":"2019","unstructured":"Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry. Adversarial examples are not bugs, they are features. arXiv preprint arXiv:1905.02175, 2019."},{"key":"e_1_3_2_2_21_1","volume-title":"International Conference on Machine Learning(ICML)","author":"Khani Fereshte","year":"2020","unstructured":"Fereshte Khani and Percy Liang. Feature noise induces loss discrepancy across groups. In International Conference on Machine Learning(ICML), 2020."},{"key":"e_1_3_2_2_22_1","volume-title":"Maximum weighted loss discrepancy. arXiv preprint arXiv:1906.03518","author":"Khani Fereshte","year":"2019","unstructured":"Fereshte Khani, Aditi Raghunathan, and Percy Liang. Maximum weighted loss discrepancy. arXiv preprint arXiv:1906.03518, 2019."},{"key":"e_1_3_2_2_23_1","first-page":"656","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","author":"Kilbertus Niki","year":"2017","unstructured":"Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Sch\u00f6lkopf. Avoiding discrimination through causal reasoning. In Advances in Neural Information Processing Systems (NeurIPS), pages 656--666, 2017."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3328526.3329621"},{"key":"e_1_3_2_2_25_1","first-page":"4069","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","author":"Kusner Matt J","year":"2017","unstructured":"Matt J Kusner, Joshua R Loftus, Chris Russell, and Ricardo Silva. Counterfactual fairness. In Advances in Neural Information Processing Systems (NeurIPS), pages 4069--4079, 2017."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"e_1_3_2_2_28_1","volume-title":"Causal reasoning for algorithmic fairness. arXiv preprint arXiv:1805.05859","author":"Loftus Joshua R","year":"2018","unstructured":"Joshua R Loftus, Chris Russell, Matt J Kusner, and Ricardo Silva. Causal reasoning for algorithmic fairness. arXiv preprint arXiv:1805.05859, 2018."},{"key":"e_1_3_2_2_29_1","volume-title":"The variational fair autoencoder. arXiv preprint arXiv:1511.00830","author":"Louizos Christos","year":"2015","unstructured":"Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard Zemel. The variational fair autoencoder. arXiv preprint arXiv:1511.00830, 2015."},{"key":"e_1_3_2_2_30_1","volume-title":"Learning adversarially fair and transferable representations. arXiv preprint arXiv:1802.06309","author":"Madras David","year":"2018","unstructured":"David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel. Learning adversarially fair and transferable representations. arXiv preprint arXiv:1802.06309, 2018."},{"key":"e_1_3_2_2_31_1","volume-title":"Towards deep learning models resistant to adversarial attacks (published at ICLR","author":"Madry Aleksander","year":"2018","unstructured":"Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning models resistant to adversarial attacks (published at ICLR 2018). arXiv, 2017."},{"key":"e_1_3_2_2_32_1","volume-title":"Cheng Soon Ong, and Robert C Williamson. Provably fair representations. arXiv preprint arXiv:1710.04394","author":"McNamara Daniel","year":"2017","unstructured":"Daniel McNamara, Cheng Soon Ong, and Robert C Williamson. Provably fair representations. arXiv preprint arXiv:1710.04394, 2017."},{"key":"e_1_3_2_2_33_1","volume-title":"The generalization error of random features regression: Precise asymptotics and double descent curve. arXiv preprint arXiv:1908.05355","author":"Mei Song","year":"2019","unstructured":"Song Mei and Andrea Montanari. The generalization error of random features regression: Precise asymptotics and double descent curve. arXiv preprint arXiv:1908.05355, 2019."},{"key":"e_1_3_2_2_34_1","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","author":"Najafi Amir","year":"2019","unstructured":"Amir Najafi, Shin ichi Maeda, Masanori Koyama, and Takeru Miyato. Robustness to adversarial perturbations in learning from incomplete data. In Advances in Neural Information Processing Systems (NeurIPS), 2019."},{"key":"e_1_3_2_2_35_1","volume-title":"Adversarial robustness may be at odds with simplicity. arXiv preprint arXiv:1901.00532","author":"Nakkiran Preetum","year":"2019","unstructured":"Preetum Nakkiran. Adversarial robustness may be at odds with simplicity. arXiv preprint arXiv:1901.00532, 2019."},{"key":"e_1_3_2_2_36_1","volume-title":"Deep double descent: Where bigger models and more data hurt. arXiv preprint arXiv:1912.02292","author":"Nakkiran Preetum","year":"2019","unstructured":"Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, and Ilya Sutskever. Deep double descent: Where bigger models and more data hurt. arXiv preprint arXiv:1912.02292, 2019."},{"key":"e_1_3_2_2_37_1","first-page":"4951","volume-title":"International Conference on Machine Learning (ICML)","author":"Oymak Samet","year":"2019","unstructured":"Samet Oymak and Mahdi Soltanolkotabi. Overparameterized nonlinear learning: Gradient descent takes the shortest path? In International Conference on Machine Learning (ICML), pages 4951--4960, 2019."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00842"},{"key":"e_1_3_2_2_39_1","volume-title":"International Conference on Machine Learning (ICML)","author":"Raghunathan Aditi","year":"2020","unstructured":"Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, and Percy Liang. Understanding and mitigating the tradeoff between robustness and accuracy. In International Conference on Machine Learning (ICML), 2020."},{"key":"e_1_3_2_2_40_1","volume-title":"International Conference on Knowledge Discovery and Data Mining (KDD)","author":"Ribeiro Marco Tulio","year":"2016","unstructured":"Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. \"Why Should I Trust You?\": Explaining the predictions of any classifier. In International Conference on Knowledge Discovery and Data Mining (KDD), 2016."},{"key":"e_1_3_2_2_41_1","volume-title":"Mitigating gender bias in natural language processing: Literature review. arXiv preprint arXiv:1906.08976","author":"Sun Tony","year":"2019","unstructured":"Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, and William Yang Wang. Mitigating gender bias in natural language processing: Literature review. arXiv preprint arXiv:1906.08976, 2019."},{"key":"e_1_3_2_2_42_1","volume-title":"International Conference on Learning Representations (ICLR)","author":"Szegedy Christian","year":"2014","unstructured":"Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. Intriguing properties of neural networks. In International Conference on Learning Representations (ICLR), 2014."},{"key":"e_1_3_2_2_43_1","volume-title":"There is no free lunch in adversarial robustness (but there are unexpected benefits). arXiv preprint arXiv:1805.12152","author":"Tsipras Dimitris","year":"2018","unstructured":"Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry. There is no free lunch in adversarial robustness (but there are unexpected benefits). arXiv preprint arXiv:1805.12152, 2018."},{"key":"e_1_3_2_2_44_1","volume-title":"International Conference on Learning Representations (ICLR)","author":"Tsipras Dimitris","year":"2019","unstructured":"Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry. Robustness may be at odds with accuracy. In International Conference on Learning Representations (ICLR), 2019."},{"key":"e_1_3_2_2_45_1","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","author":"Uesato Jonathan","year":"2019","unstructured":"Jonathan Uesato, Jean-Baptiste Alayrac, Po-Sen Huang, Robert Stanforth, Alhussein Fawzi, and Pushmeet Kohli. Are labels required for improving adversarial robustness? In Advances in Neural Information Processing Systems (NeurIPS), 2019."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00541"},{"key":"e_1_3_2_2_47_1","first-page":"1920","volume-title":"Conference on Learning Theory (COLT)","author":"Woodworth Blake","year":"2017","unstructured":"Blake Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, and Nathan Srebro. Learning non-discriminatory predictors. In Conference on Learning Theory (COLT), pages 1920--1953, 2017."},{"key":"e_1_3_2_2_48_1","volume-title":"Noise or signal: The role of image backgrounds in object recognition. arXiv preprint arXiv:2006.09994","author":"Xiao Kai","year":"2020","unstructured":"Kai Xiao, Logan Engstrom, Andrew Ilyas, and Aleksander Madry. Noise or signal: The role of image backgrounds in object recognition. arXiv preprint arXiv:2006.09994, 2020."},{"key":"e_1_3_2_2_49_1","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","author":"Yin Dong","year":"2019","unstructured":"Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens, Ekin D Cubuk, and Justin Gilmer. A fourier perspective on model robustness in computer vision. In Advances in Neural Information Processing Systems (NeurIPS), 2019."},{"key":"e_1_3_2_2_50_1","volume-title":"Sensei: Sensitive set invariance for enforcing individual fairness. arXiv preprint arXiv:2006.14168","author":"Yurochkin Mikhail","year":"2020","unstructured":"Mikhail Yurochkin and Yuekai Sun. Sensei: Sensitive set invariance for enforcing individual fairness. arXiv preprint arXiv:2006.14168, 2020."},{"key":"e_1_3_2_2_51_1","first-page":"325","volume-title":"International Conference on Machine Learning (ICML)","author":"Zemel Richard","year":"2013","unstructured":"Richard Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork. Learning fair representations. In International Conference on Machine Learning (ICML), pages 325--333, 2013."},{"key":"e_1_3_2_2_52_1","volume-title":"International Conference on Machine Learning(ICML)","author":"Zhang Hongyang","year":"2019","unstructured":"Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P Xing, Laurent El Ghaoui, and Michael I Jordan. Theoretically principled trade-off between robustness and accuracy. In International Conference on Machine Learning(ICML), 2019."},{"key":"e_1_3_2_2_53_1","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","author":"Zhao H.","year":"2019","unstructured":"H. Zhao and Geoff Gordon. Inherent tradeoffs in learning fair representations. In Advances in Neural Information Processing Systems (NeurIPS), 2019."},{"key":"e_1_3_2_2_54_1","volume-title":"Gender bias in coreference resolution: Evaluation and debiasing methods","author":"Zhao Jieyu","year":"2018","unstructured":"Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordo\u00f1ez, and Kai-Wei Chang. Gender bias in coreference resolution: Evaluation and debiasing methods. In North American Association for Computational Linguistics (NAACL), 2018."},{"key":"e_1_3_2_2_55_1","volume-title":"Learning gender-neutral word embeddings. arXiv preprint arXiv:1809.01496","author":"Zhao Jieyu","year":"2018","unstructured":"Jieyu Zhao, Yichao Zhou, Zeyu Li, Wei Wang, and Kai-Wei Chang. Learning gender-neutral word embeddings. arXiv preprint arXiv:1809.01496, 2018."}],"event":{"name":"FAccT '21: 2021 ACM Conference on Fairness, Accountability, and Transparency","location":"Virtual Event Canada","acronym":"FAccT '21","sponsor":["ACM Association for Computing Machinery"]},"container-title":["Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3442188.3445883","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3442188.3445883","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:48:56Z","timestamp":1750193336000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3442188.3445883"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3]]},"references-count":55,"alternative-id":["10.1145\/3442188.3445883","10.1145\/3442188"],"URL":"https:\/\/doi.org\/10.1145\/3442188.3445883","relation":{},"subject":[],"published":{"date-parts":[[2021,3]]},"assertion":[{"value":"2021-03-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}