{"type":"ScholarlyArticle","title":"Achieving Outcome Fairness in Machine Learning Models for Social Decision Problems","authors":[{"name":"Boli Fang","slug":"boli-fang","given":"Boli","family":"Fang","affiliation":"Dept of Computer Science , Indiana University"},{"name":"Miao Jiang","slug":"miao-jiang","given":"Miao","family":"Jiang","affiliation":"Dept of Intelligent Systems Engineering , Indiana University"},{"name":"Pei-yi Cheng","slug":"pei-yi-cheng","given":"Pei-yi","family":"Cheng","affiliation":"Dept of Computer Science , Indiana University"},{"name":"Jerry Shen","slug":"jerry-shen","given":"Jerry","family":"Shen","affiliation":"Sol Price School of Public Policy , University of Southern California"},{"name":"Yi Fang","slug":"yi-fang","given":"Yi","family":"Fang","affiliation":"Dept of Computer Science and Engineering , Santa Clara University"}],"abstract":"Effective complements to human judgment, artificial intelligence techniques have started to aid human decisions in complicated social decision problems across the world. Automated machine learning/deep learning(ML/DL) classification models, through quantitative modeling, have the potential to improve upon human decisions in a wide range of decision problems on social resource allocation such as Medicaid and Supplemental Nutrition Assistance Program(SNAP, commonly referred to as Food Stamps). However, given the limitations in ML/DL model design, these algorithms may fail to leverage various factors for decision making, resulting in improper decisions that allocate resources to individuals who may not be in the most need of such resource. In view of such an issue, we propose in this paper the strategy of fairgroups, based on the legal doctrine of disparate impact, to improve fairness in prediction outcomes. Experiments on various datasets demonstrate that our fairgroup construction method effectively boosts the fairness in automated decision making, while maintaining high prediction accuracy.","doi":"10.24963/ijcai.2020/62","url":"https://ijcai.org/proceedings/2020/62","pdf":"https://ijcai.org/proceedings/2020/0062.pdf","markdown":"https://ijcai.org/proceedings/2020/62.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/62","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"AI Ethics","track":"Main track","number":62,"pages":{"first":444,"last":450},"keywords":["AI Ethics: Moral Decision Making","AI Ethics: Societal Impact of AI","Machine Learning: Classification","AI Ethics: Fairness"],"videos":[],"references":[{"position":1,"raw":"[Bureau, 2017] US Census Bureau. American community survey 2017 5-year estimate. 2017.","title":"US Census Bureau. American community survey","authors":null,"year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chierichetti et al., 2017] Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, and Sergei Vassilvitskii. Fair clustering through fairlets. In Advances in Neural Information Pro- cessing Systems, pages 5029-5037, 2017.","title":"Fair clustering through fairlets","authors":"Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Corbett-Davies and Goel, 2018] Sam Corbett-Davies and Sharad Goel. The measure and mismeasure of fairness: A critical review of fair machine learning. arXiv preprint arXiv:1808.00023, 2018.","title":"The measure and mismeasure of fairness: A critical review of fair machine learning","authors":"Corbett-Davies, Sam Goel, Sharad Corbett-Davies, Goel","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Feldman et al., 2015] Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkata- subramanian. Certifying and removing disparate impact. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 259-268. ACM, 2015.","title":"Certifying and removing disparate impact","authors":"Michael Feldman, A Sorelle, John Friedler, Carlos Moeller, Suresh Scheidegger, Venkatasubramanian","year":"2015","venue":"Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Joseph et al., 2016] Matthew Joseph, Michael Kearns, Jamie H Morgenstern, and Aaron Roth. Fairness in learning: Classic and contextual bandits. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems 29, pages 325-333.","title":"Fairness in learning: Classic and contextual bandits","authors":"Matthew Joseph, Michael Kearns, Jamie H Morgenstern, Aaron Roth","year":null,"venue":"Advances in Neural Information Processing Systems 29","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"Curran Associates, Inc., 2016.","title":null,"authors":"Curran Associates, Inc","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Kamishima et al., 2011] Toshihiro Kamishima, Shotaro Akaho, and Jun Sakuma. Fairness-aware learning through regularization approach. In 2011 IEEE 11th International Conference on Data Mining Workshops, pages 643-650. IEEE, 2011.","title":"Fairness-aware learning through regularization approach","authors":"Toshihiro Kamishima, Shotaro Akaho, Jun Sakuma","year":"2011","venue":"IEEE 11th International Conference on Data Mining Workshops","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Kleinberg et al., 2016] Jon Kleinberg, Sendhil Mul- lainathan, and Manish Raghavan. Inherent trade-offs in the fair determination of risk scores. arXiv preprint arXiv:1609.05807, 2016.","title":"Inherent trade-offs in the fair determination of risk scores","authors":"Kleinberg","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Morse, 2018] Susan Morse. Artificial intelligence helps in- surers identify medicare members who also qualify for medicaid, Nov 2018. [Nations, 2016] United Nations. 17 goals to transform the world for persons with disabilities. 2016.","title":"Artificial intelligence helps insurers identify medicare members who also qualify for medicaid","authors":"Susan Morse","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Rösner and Schmidt, 2018] Clemens Rösner and Melanie Schmidt. Privacy preserving clustering with constraints. 2018.","title":"Privacy preserving clustering with constraints","authors":"Schmidt Rösner, Clemens Rösner, Melanie Schmidt","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Schmidt et al., 2018] Melanie Schmidt, Chris Schwiegelshohn, and Christian Sohler. Fair coresets and streaming algorithms for fair k-means clustering. 2018.","title":"Fair coresets and streaming algorithms for fair k-means clustering","authors":"Melanie Schmidt, Chris Schwiegelshohn, Christian Sohler","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Zadrozny, 2004] Bianca Zadrozny. Learning and evaluat- ing classifiers under sample selection bias. In Proceedings of the Twenty-first International Conference on Machine Learning, ICML '04, pages 114-, New York, NY, USA, 2004. ACM.","title":"Learning and evaluating classifiers under sample selection bias","authors":"Bianca Zadrozny","year":"2004","venue":"Proceedings of the Twenty-first International Conference on Machine Learning, ICML '04","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Zafar et al., 2015] Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi. Fair- ness constraints: Mechanisms for fair classification. arXiv preprint arXiv:1507.05259, 2015.","title":"Fairness constraints: Mechanisms for fair classification","authors":"Zafar","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Zhu and Shi, 2015] Haoyu Zhu and Yuhui Shi. Brain storm optimization algorithms with k-medians clustering algo- rithms. In 2015 Seventh International Conference on Ad- vanced Computational Intelligence (ICACI), pages 107- 110. IEEE, 2015.","title":"Brain storm optimization algorithms with k-medians clustering algorithms","authors":"Shi ; ] Haoyu Zhu, Yuhui Zhu, Shi","year":"2015","venue":"Seventh International Conference on Advanced Computational Intelligence (ICACI)","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Zink and Rose, 2019] Anna Zink and Sherri Rose. Fair regression for health care spending. arXiv preprint arXiv:1901.10566, 2019.","title":"Fair regression for health care spending","authors":"Anna Zink, Sherri Rose","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}