DENG Wei,XING Yuhan,LI Yifan,et al.Survey on fair machine learning[J].CAAI Transactions on Intelligent Systems,2020,15(3):578-586.[doi:10.11992/tis.202007004]





Survey on fair machine learning
邓蔚12 邢钰晗1 李逸凡1 李振华3 王国胤2
1. 西南财经大学 统计研究中心,四川 成都 611130;
2. 重庆邮电大学 计算智能重庆市重点实验室,重庆 400065;
3. 西南财经大学 金融学院,四川 成都 611130
DENG Wei12 XING Yuhan1 LI Yifan1 LI Zhenhua3 WANG Guoyin2
1. Center of Statistical Research, Southwestern University of Finance and Economics, Chengdu 611130, China;
2. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
3. School of Finance, Southwestern University of Finance and Economics, Chengdu 611130, China
algorithmic ethicsalgorithmic discriminationfairnessfair machine learningfair indicatorfair designfair datasetdynamicity
With the widespread applications of machine learning in our society, the problems of discrimination have caused widespread social controversy. It gradually arouses strong interests in fair machine learning in the industry and academia. Nowdays the deep understanding of the basic issues related to fairness and mechanism of fair machine learning is still in their infancy. We makes a survey on fair machine learning. Starting from the definitions of fairness, it compares the different difinitions on fairness in different problems. Common datasets are also summarized. And the issues of fairness is analyzed. We classify and compare the existing methods of achieving fairness. Finally, we summarizes the problems in current fairness machine learning research and propose the key problems and important challenges in the future.


[1] 高庆吉, 赵志华, 徐达, 等. 语音情感识别研究综述[J]. 智能系统学报, 2020, 15(1): 1-13
GAO Qingji, ZHAO Zhihua, XU Da, et al. Review on speech emotion recognition research[J]. CAAI transactions on intelligent systems, 2020, 15(1): 1-13
[2] YOCHUM P, 常亮, 古天龙, 等. 基于位置和开放链接数据的旅游推荐系统综述[J]. 智能系统学报, 2020, 15(1): 25-32
YOCHUM P, CHANG Liang, GU Tianlong, et al. A review of linked open data in location-based recommendation system in the tourism domain[J]. CAAI transactions on intelligent systems, 2020, 15(1): 25-32
[3] 常乐, 杨忠, 张秋雁, 等. 悬挂负载空中机器人的抗摆控制[J]. 应用科技, 2020, 47(2): 17-22
CHANG Le, YANG Zhong, ZHANG Qiuyan, et al. Anti-swing control research of aerial robot with suspended load[J]. Applied science and technology, 2020, 47(2): 17-22
[4] KHANDANI A E, KIM A J, LO A W. Consumer credit-risk models via machine-learning algorithms[J]. Journal of banking and finance, 2010, 34(11): 2767-2787.
[5] BRENNAN T, DIETERICH W, EHRET B. Evaluating the predictive validity of the compas risk and needs assessment system[J]. Criminal justice and behavior, 2009, 36(1): 21-40.
[6] MAHONEY J F, MOHEN J M. Method and system for loan origination and underwriting[P]. US: 7287008.1, 2007-10-23.
[7] KEARNS M, ROTH A. The ethical algorithm: the science of socially aware algorithm design[M]. New York: Oxford University Press, 2019: 11.
[8] IEEE新版“人工智能设计的伦理准则”白皮书全球重磅发布[EB/OL]. (2017-12-15)[2020-07-26] https://www.sohu.com/a/210646713_468720.
[9] Publications Office of the EU[EB/OL]. (2018-03-09)[2020-07-26] https://op.europa.eu/en/publication-detail/-/publication/dfebe62e-4ce9-11e8-be1d-01aa75ed71a1/language-en/format-PDF/source-78120382.
[10] 吴沈括, 周洁, 杨滢滢. 人工智能伦理与数据保护宣言[EB/OL]. (2018-10-30)[2020-07-26]. http://www.yidianzixun.com/m/article/0KOD5oLY.
[11] OECD Principles on AI[EB/OL]. [2020-07-26] https://www.oecd.org/going-digital/ai/principles/.
[12] G20 ministerial statement on trade and digital economy[EB/OL]. (2019-06-09)[2020-07-26] http://www.g20.utoronto.ca/2019/2019-g20-trade.html.
[13] 国家新一代人工智能治理专业委员会. 发展负责任的人工智能: 新一代人工智能治理原则发布[EB/OL]. (2019-06-17)[2020-07-26] http://www.most.gov.cn/kjbgz/201906/t20190617_147107.htm.
[14] FRIEDLER S A, SCHEIDEGGER C, VENKATASUBRAMANIAN S, et al. A comparative study of fairness-enhancing interventions in machine learning[C]//Proceedings of the Conference on Fairness, Accountability, and Transparency. New York, USA, 2019: 329-338.
[15] KUSNER M, LOFTUS J, RUSSEL C, et al. Counterfactual fairness[C]//Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017). Long Beach, USA, 2017.
[16] GRGI?-HLA?A N, ZAFAR M B, GUMMADI K P, et al. The case for process fairness in learning: feature selection for fair decision making[C]//Symposium on Machine Learning and the Law at the 29th Conference on Neural Information Processing Systems. Barcelona, Spain, 2016: 1.
[17] DWORK C, HARDT M, PITASSI T, et al. Fairness through awareness[C]//Proceedings of the 3rd Innovations in Theoretical Computer Science Conference. New York, USA, 2012: 214-226.
[18] JOSEPH M, KEARNS M, MORGENSTERN J, et al. Rawlsian fairness for machine learning [DB/OL]. (2017-06-29)[2020-08-07] arXiv preprint arXiv:1610. 09559V2, arxiv.org/abs/1610.09559v2, 2016.
[19] LOUIZOS C, SWERSKY K, LI Yujia, et al. The variational fair autoencoder[C]//Proceedings of the 4th International Conference on Learning Representations. San Juan, Puerto Rico, 2016.
[20] ZEMEL R, WU Yu, SWERSKY K, et al. Learning fair representations[C]//Proceedings of the 30th International Conference on International Conference on Machine Learning. Atlanta, USA, 2013: 325-333.
[21] KIM M P, KOROLOVA A, ROTHBLUM G N, et al. Preference-informed fairness[C]//Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. New York, USA, 2020: 546.
[22] ZAFA M B, VALERA I, ROGRIGUEZ M G, et al. Fairness constraints: mechanisms for fair classification[C]//Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. Lille, France, 2017: 962-970.
[23] ZAFAR M B, VALERA I, RODRIGUEZ M G, et al. Fairness beyond disparate treatment & disparate impact: learning classification without disparate mistreatment[C]//Proceedings of the 26th International Conference on World Wide Web. Perth, Australia, 2017: 1171-1180.
[24] BERETTA E, SANTANGELO A, LEPRI B, et al. The invisible power of fairness. How machine learning shapes democracy [DB/OL]. (2019-03-22)[2020-07-26] arXiv preprint arXiv:1903.09493v1, https://arxiv.org/abs/1903.09493, 2019.
[25] CHOULDECHOVA A. Fair prediction with disparate impact: a study of bias in recidivism prediction instruments[J]. Big data, 2017, 5(2): 153-163.
[26] BAROCAS S, SELBST A D. Big data’s disparate impact[J]. California law review, 2016, 104: 671-732.
[27] KEARNS M, ROTH A, WU Z S. Meritocratic fairness for cross-population selection[C]//Proceedings of the 34th International Conference on Machine Learning. Sydney, Australia, 2017: 1828-1836.
[28] KLEINBERG J, MULLAINATHAN S, RAGHAVAN M. Inherent trade-offs in the fair determination of risk scores[C]//Proceedings of the 8th Innovations in Theoretical Computer Science Conference. Dagstuhl, Germany, 2017.
[29] Supreme Court of the United States. Ricci v. DeStefano [EB/OL]. (2009-06-29)[ 2020-08-07]. 557 U.S. 557,https://supreme.justia.com/cases/federal/us/557/557/, 2009.
[30] Adult data[EB/OL]. [2020-07-26]. http://tinyurl.com/UCI-Adult, 1996.
[31] LICHMAN M. UCI machine learning repository[EB/OL]. (2013)[2020-07-26]. http://archive.ics.uci.edu/ml, 2013.
[32] ANGWIN J, LARSON J, MATTU S, et al. Machine bias. risk assessments in criminal sentencing[EB/OL]. (2016-05-23)[2020-07-26] https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing, 2016.
[33] Bank Marketing Data Set [EB/OL]. (2012-02-14) [2020-07-26] https://archive.ics.uci.edu/ml/datasets/Bank%2BMarketing, 2012.
[34] KHADEMI A, LEE S, FOLEY D, et al. Fairness in algorithmic decision making: an excursion through the lens of causality[C]//The World Wide Web Conference. San Francisco, USA, 2019: 2907-2914.
[35] FELDMAN M, FRIEDLER S A, MOELLER J, et al. Certifying and removing disparate impact[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA, 2015: 259-268.
[36] KAMIRAN F, CALDERS T. Data preprocessing techniques for classification without discrimination[J]. Knowledge and information systems, 2012, 33(1): 1-33.
[37] CALMON F P, WEI D, VINZAMURI B, et al. Optimized pre-processing for discrimination prevention[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook, USA, 2017: 3995-4004.
[38] KAMISHIMA T, AKAHO S, ASOH H, et al. Fairness-aware classifier with prejudice remover regularizer[M]//FLACH P A, DE BIE T, CRISTIANINI N. Machine Learning and Knowledge Discovery in Databases. Berlin: Springer, 2012: 35-50.
[39] CALDERS T, VERWER S. Three naive Bayes approaches for discrimination-free classification[J]. Data mining and knowledge discovery, 2010, 21(2): 277-292.
[40] BOSE A J, HAMILTON W. Compositional fairness constraints for graph embeddings [DB/OL]. (2019-07-16)[2020-07-07] https://arxiv.org/abs/1905.10674, 2019.
[41] HARDT M, PRICE E, SREBRO N. Equality of opportunity in supervised learning[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems. Red Hook, USA, 2016: 3315-3323.
[42] KAMIRAN F, CALDERS T. Classifying without discriminating[C]//Proceedings of 2009 2nd International Conference on Computer, Control and Communication. Karachi, Pakistan, 2009.
[43] WOODWORTH B, GUNASEKAR S, OHANNESSIAN M I, et al. Learning non-discriminatory predic-tors [EB/OL]. (2017-11-01)[2020-07-07] https://arxiv.org/abs/1702.06081, 2017.
[44] CORBETT-DAVIES S, GOEL S. The measure and mismeasure of fairness: a critical review of fair machine learning [DB/OL]. (2018-08-14)[2020-07-07] https://arxiv.org/abs/1808. 00023, 2018.
[45] KANNAN S, KEARNS M, MORGENSTERN J, et al. Fairness incentives for myopic agents[C]//Proceedings of the 2017 ACM Conference on Economics and Computation. New York, USA, 2017: 369-386.
[46] CORBETT-DAVIES S, PIERSON E, FELLER A, et al. Algorithmic decision making and the cost of fairness[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA, 2017: 797-806.
[47] D’AMOUR A, SRINIVASAN H, ATWOOD J, et al. Fairness is not static: deeper understanding of long term fairness via simulation studies[C]//Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. Barcelona, Spain, 2020: 525-534.
[48] Google/ml-fairness-gym[EB/OL]. [2020-07-26] https://github.com/google/ml-fairness-gym/.
[49] KUPPAM S, MCKENNA R, PUJOL D, et al. Fair decision making using privacy-protected data [DE/OL]. (2020-01-24)[2020-08-07] https://arxiv.org/abs/1905.12744, 2020.
[50] SLACK D, FRIEDLER S A, GIVENTAL E. Fairness warnings and fair-MAML: learning fairly with minimal data[C]//Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. Barcelona, Spain, 2019: 200-209.
[51] GANCHEV K, KEARNS M, NEVMYVAKA Y, et al. Censored exploration and the dark pool problem[C]//Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. Arlington, USA, 2009: 185-194.
[52] DONAHUE K, KLEINBERG J. Fairness and utilization in allocating resources with uncertain demand[C]//Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. New York, USA, 2020: 658-668.
[53] DEVRIES T, MISRA I, WANG C, et al. 2019. Does object recognition work for everyone? [EB/OL]. (2019-06-18)[2020-07-07] https://arxiv.org/abs/1906.02659, 2019.
[54] STOCK P, CISSE M. ConvNets and ImageNet beyond accuracy: understanding mistakes and uncovering biases[C]//Proceedings of the 15th European Conference on Computer Vision. Munich, Germany, 2018: 498-512.
[55] DULHANTY C, WONG A. Auditing imageNet: towards a model-driven framework for annotating demographic attributes of large-scale image datasets [EB/OL]. (2019-06-04)[2020-07-07] https://arxiv.org/abs/1905.01347, 2019.
[56] YANG Kaiyu, QINAMI K, FEI-FEI L, et al. Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy[C]//Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. New York, USA, 2020: 547-558.
[57] BORDIA S, BOWMAN S R. Identifying and reducing gender bias in word-level language models[C]//Proceedings of the 9th American Chapter of the Association for Computational Linguistics. Minneapolis, Minnesota, 2019: 7-15.
[58] GREEN B, CHEN Yiling. Disparate interactions: an algorithm-in-the-loop analysis of fairness in risk assessments[C]//Proceedings of the Conference on Fairness, Accountability, and Transparency. Atlanta, USA, 2019: 90-99.
[59] SONG Jiaming, KALLURI P, GROVER A, et al. Learning Controllable Fair Representations[C]//Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics. Naha, Japan, 2019: 2164-2173.
[60] LIU L T, DEAN S, ROLF E, et al. Delayed impact of fair machine learning[C]//Proceedings of the 35th International Conference on Machine Learning. Stockholm, Sweden, 2018: 3150-3158.


更新日期/Last Update: 1900-01-01