Group fairness in non-monotone submodular maximization
Maximizing a submodular function has a wide range of applications in machine learning and data mining. One such application is data summarization whose goal is to select a small set of representative and diverse data items from a large dataset. However, data items might have sensitive attributes suc...
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| Published in: | Journal of combinatorial optimization Vol. 45; no. 3; p. 88 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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01.04.2023
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| ISSN: | 1382-6905, 1573-2886 |
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| Abstract | Maximizing a submodular function has a wide range of applications in machine learning and data mining. One such application is data summarization whose goal is to select a small set of representative and diverse data items from a large dataset. However, data items might have sensitive attributes such as race or gender, in this setting, it is important to design
fairness-aware
algorithms to mitigate potential algorithmic bias that may cause over- or under- representation of particular groups. Motivated by that, we propose and study the classic non-monotone submodular maximization problem subject to novel group fairness constraints. Our goal is to select a set of items that maximizes a non-monotone submodular function, while ensuring that the number of selected items from each group is proportionate to its size, to the extent specified by the decision maker. We develop the first constant-factor approximation algorithms for this problem. We also extend the basic model to incorporate an additional global size constraint on the total number of selected items. |
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| AbstractList | Maximizing a submodular function has a wide range of applications in machine learning and data mining. One such application is data summarization whose goal is to select a small set of representative and diverse data items from a large dataset. However, data items might have sensitive attributes such as race or gender, in this setting, it is important to design fairness-aware algorithms to mitigate potential algorithmic bias that may cause over- or under- representation of particular groups. Motivated by that, we propose and study the classic non-monotone submodular maximization problem subject to novel group fairness constraints. Our goal is to select a set of items that maximizes a non-monotone submodular function, while ensuring that the number of selected items from each group is proportionate to its size, to the extent specified by the decision maker. We develop the first constant-factor approximation algorithms for this problem. We also extend the basic model to incorporate an additional global size constraint on the total number of selected items. Maximizing a submodular function has a wide range of applications in machine learning and data mining. One such application is data summarization whose goal is to select a small set of representative and diverse data items from a large dataset. However, data items might have sensitive attributes such as race or gender, in this setting, it is important to design fairness-aware algorithms to mitigate potential algorithmic bias that may cause over- or under- representation of particular groups. Motivated by that, we propose and study the classic non-monotone submodular maximization problem subject to novel group fairness constraints. Our goal is to select a set of items that maximizes a non-monotone submodular function, while ensuring that the number of selected items from each group is proportionate to its size, to the extent specified by the decision maker. We develop the first constant-factor approximation algorithms for this problem. We also extend the basic model to incorporate an additional global size constraint on the total number of selected items. |
| ArticleNumber | 88 |
| Author | Yuan, Jing Tang, Shaojie |
| Author_xml | – sequence: 1 givenname: Jing orcidid: 0000-0001-6407-834X surname: Yuan fullname: Yuan, Jing organization: Department of Computer Science and Engineering, University of North Texas – sequence: 2 givenname: Shaojie orcidid: 0000-0001-9261-5210 surname: Tang fullname: Tang, Shaojie email: shaojie.tang@utdallas.edu organization: Naveen Jindal School of Management, University of Texas at Dallas |
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| Cites_doi | 10.24963/ijcai.2019/831 10.1145/1374376.1374384 10.2307/2082518 10.24963/ijcai.2018/20 10.1007/978-3-031-16081-3_1 10.1109/FOCS.2011.46 10.4324/9781315263298 10.1016/j.orl.2019.10.013 10.1142/S1793830921500361 10.1007/s10878-023-01035-4 10.1007/978-3-031-46826-1_13 10.1145/2396761.2396857 10.1145/2020408.2020479 10.1007/s10878-022-00965-9 10.1145/2090236.2090255 10.1109/ICCV.2007.4408853 10.1137/1.9781611973730.80 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. |
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| Keywords | Approximation algorithm Submodular optimization Group fairness |
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| References | Tang S, Yuan J (2023) Beyond Submodularity: A unified framework of randomized set selection with group fairness constraints (Under Review) Feldman M, Naor J, Schwartz R (2011) A unified continuous greedy algorithm for submodular maximization. In: 2011 IEEE 52nd annual symposium on foundations of computer science, pp 570–579. IEEE TangSYuanJInfluence maximization with partial feedbackOper Res Lett2020481242810.1016/j.orl.2019.10.013071659784030316 Zafar MB, Valera I, Rogriguez MG, Gummadi KP (2017) Fairness constraints: mechanisms for fair classification. In: Artificial intelligence and statistics, pp 962–970. PMLR Gotovos A, Karbasi A, Krause A (2015) Non-monotone adaptive submodular maximization. In: Twenty-fourth international joint conference on artificial intelligence MonroeBLFully proportional representationAm Polit Sci Rev199589492594010.2307/2082518 Celis E, Keswani V, Straszak D, Deshpande A, Kathuria T, Vishnoi N (2018) Fair and diverse dpp-based data summarization. In: International conference on machine learning, pp 716–725. PMLR Chierichetti F, Kumar R, Lattanzi S, Vassilvtiskii S (2019) Matroids, matchings, and fairness. In: The 22nd international conference on artificial intelligence and statistics, pp 2212–2220. PMLR Dueck D, Frey BJ (2007) Non-metric affinity propagation for unsupervised image categorization. In: 2007 IEEE 11th international conference on computer vision, pp 1–8. IEEE ShiGGuSWuWk-submodular maximization with two kinds of constraintsDiscr Math Algorithms Appl20211304215003610.1142/S17938309215003611475.900864284039 Celis LE, Huang L, Vishnoi NK (2018) Multiwinner voting with fairness constraints. In: Proceedings of the 27th international joint conference on artificial intelligence, pp 144–151 Tang S, Yuan J (2022) Group equility in adaptive submodular maximization. arXiv preprint arXiv:2207.03364 El HalabiMMitrovićSNorouzi-FardATardosJTarnawskiJMFairness in streaming submodular maximization: algorithms and hardnessAdv Neural Inf Process Syst2020331360913622 GolovinDKrauseAAdaptive submodularity: theory and applications in active learning and stochastic optimizationJ Artif Intell Res2011424274861230.901412874807 El-Arini K, Guestrin C (2011) Beyond keyword search: discovering relevant scientific literature. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 439–447 Dwork C, Hardt M, Pitassi T, Reingold O, Zemel R (2012) Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference Tang S, Yuan J (2021) Adaptive regularized submodular maximization. In: 32nd international symposium on algorithms and computation (ISAAC 2021). Schloss Dagstuhl-Leibniz-Zentrum für Informatik BiddleDAdverse impact and test validation: a practitioner’s guide to valid and defensible employment testing2017OxfordshireRoutledge10.4324/9781315263298 Tsang A, Wilder B, Rice E, Tambe M, Zick Y (2019) Group-fairness in influence maximization. arXiv preprint arXiv:1903.00967 Buchbinder N, Feldman M, Naor J, Schwartz R (2014) Submodular maximization with cardinality constraints. In: Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete algorithms, pp 1433–1452. SIAM Mirzasoleiman B, Badanidiyuru A, Karbasi A (2016) Fast constrained submodular maximization: personalized data summarization. In: ICML, pp 1358–1367 Gu S, Gao C, Wu W (2022) A binary search double greedy algorithm for non-monotone DR-submodular maximization. In: Algorithmic aspects in information and management: 16th international conference, AAIM 2022, Guangzhou, China, 13–14 Aug, 2022, proceedings. Springer, pp. 3–14 Joseph M, Kearns M, Morgenstern JH, Roth A (2016) Fairness in learning: classic and contextual bandits. Adv. Neural Inf. Process. Syst. 29 Das A, Kempe D (2008) Algorithms for subset selection in linear regression. In: Proceedings of the fortieth annual ACM symposium on Theory of computing, pp 45–54 Tang S, Yuan J, Mensah-Boateng T (2023) Achieving long-term fairness in submodular maximization through randomization (Under Review) Sipos R, Swaminathan A, Shivaswamy P, Joachims T (2012) Temporal corpus summarization using submodular word coverage. In: Proceedings of the 21st ACM international conference on Information and knowledge management, pp 754–763 1019_CR8 1019_CR7 1019_CR9 1019_CR11 1019_CR13 1019_CR15 1019_CR14 1019_CR16 S Tang (1019_CR20) 2020; 48 1019_CR19 1019_CR2 G Shi (1019_CR18) 2021; 13 1019_CR4 1019_CR3 1019_CR6 D Golovin (1019_CR12) 2011; 42 1019_CR5 D Biddle (1019_CR1) 2017 1019_CR22 1019_CR21 1019_CR24 1019_CR23 1019_CR26 1019_CR25 BL Monroe (1019_CR17) 1995; 89 M El Halabi (1019_CR10) 2020; 33 |
| References_xml | – reference: Chierichetti F, Kumar R, Lattanzi S, Vassilvtiskii S (2019) Matroids, matchings, and fairness. In: The 22nd international conference on artificial intelligence and statistics, pp 2212–2220. PMLR – reference: MonroeBLFully proportional representationAm Polit Sci Rev199589492594010.2307/2082518 – reference: Mirzasoleiman B, Badanidiyuru A, Karbasi A (2016) Fast constrained submodular maximization: personalized data summarization. In: ICML, pp 1358–1367 – reference: Sipos R, Swaminathan A, Shivaswamy P, Joachims T (2012) Temporal corpus summarization using submodular word coverage. In: Proceedings of the 21st ACM international conference on Information and knowledge management, pp 754–763 – reference: Tang S, Yuan J, Mensah-Boateng T (2023) Achieving long-term fairness in submodular maximization through randomization (Under Review) – reference: GolovinDKrauseAAdaptive submodularity: theory and applications in active learning and stochastic optimizationJ Artif Intell Res2011424274861230.901412874807 – reference: Buchbinder N, Feldman M, Naor J, Schwartz R (2014) Submodular maximization with cardinality constraints. In: Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete algorithms, pp 1433–1452. SIAM – reference: Zafar MB, Valera I, Rogriguez MG, Gummadi KP (2017) Fairness constraints: mechanisms for fair classification. In: Artificial intelligence and statistics, pp 962–970. PMLR – reference: TangSYuanJInfluence maximization with partial feedbackOper Res Lett2020481242810.1016/j.orl.2019.10.013071659784030316 – reference: El HalabiMMitrovićSNorouzi-FardATardosJTarnawskiJMFairness in streaming submodular maximization: algorithms and hardnessAdv Neural Inf Process Syst2020331360913622 – reference: Feldman M, Naor J, Schwartz R (2011) A unified continuous greedy algorithm for submodular maximization. In: 2011 IEEE 52nd annual symposium on foundations of computer science, pp 570–579. IEEE – reference: Gu S, Gao C, Wu W (2022) A binary search double greedy algorithm for non-monotone DR-submodular maximization. In: Algorithmic aspects in information and management: 16th international conference, AAIM 2022, Guangzhou, China, 13–14 Aug, 2022, proceedings. Springer, pp. 3–14 – reference: Tang S, Yuan J (2022) Group equility in adaptive submodular maximization. arXiv preprint arXiv:2207.03364 – reference: BiddleDAdverse impact and test validation: a practitioner’s guide to valid and defensible employment testing2017OxfordshireRoutledge10.4324/9781315263298 – reference: Joseph M, Kearns M, Morgenstern JH, Roth A (2016) Fairness in learning: classic and contextual bandits. Adv. Neural Inf. Process. Syst. 29 – reference: Celis E, Keswani V, Straszak D, Deshpande A, Kathuria T, Vishnoi N (2018) Fair and diverse dpp-based data summarization. In: International conference on machine learning, pp 716–725. PMLR – reference: Dwork C, Hardt M, Pitassi T, Reingold O, Zemel R (2012) Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference – reference: El-Arini K, Guestrin C (2011) Beyond keyword search: discovering relevant scientific literature. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 439–447 – reference: Das A, Kempe D (2008) Algorithms for subset selection in linear regression. In: Proceedings of the fortieth annual ACM symposium on Theory of computing, pp 45–54 – reference: Tang S, Yuan J (2023) Beyond Submodularity: A unified framework of randomized set selection with group fairness constraints (Under Review) – reference: Tsang A, Wilder B, Rice E, Tambe M, Zick Y (2019) Group-fairness in influence maximization. arXiv preprint arXiv:1903.00967 – reference: Dueck D, Frey BJ (2007) Non-metric affinity propagation for unsupervised image categorization. In: 2007 IEEE 11th international conference on computer vision, pp 1–8. IEEE – reference: Celis LE, Huang L, Vishnoi NK (2018) Multiwinner voting with fairness constraints. In: Proceedings of the 27th international joint conference on artificial intelligence, pp 144–151 – reference: Gotovos A, Karbasi A, Krause A (2015) Non-monotone adaptive submodular maximization. In: Twenty-fourth international joint conference on artificial intelligence – reference: Tang S, Yuan J (2021) Adaptive regularized submodular maximization. In: 32nd international symposium on algorithms and computation (ISAAC 2021). Schloss Dagstuhl-Leibniz-Zentrum für Informatik – reference: ShiGGuSWuWk-submodular maximization with two kinds of constraintsDiscr Math Algorithms Appl20211304215003610.1142/S17938309215003611475.900864284039 – ident: 1019_CR25 doi: 10.24963/ijcai.2019/831 – ident: 1019_CR21 – ident: 1019_CR6 doi: 10.1145/1374376.1374384 – ident: 1019_CR5 – volume: 89 start-page: 925 issue: 4 year: 1995 ident: 1019_CR17 publication-title: Am Polit Sci Rev doi: 10.2307/2082518 – ident: 1019_CR4 doi: 10.24963/ijcai.2018/20 – ident: 1019_CR14 doi: 10.1007/978-3-031-16081-3_1 – ident: 1019_CR11 doi: 10.1109/FOCS.2011.46 – ident: 1019_CR16 – volume-title: Adverse impact and test validation: a practitioner’s guide to valid and defensible employment testing year: 2017 ident: 1019_CR1 doi: 10.4324/9781315263298 – ident: 1019_CR26 – volume: 48 start-page: 24 issue: 1 year: 2020 ident: 1019_CR20 publication-title: Oper Res Lett doi: 10.1016/j.orl.2019.10.013 – volume: 13 start-page: 2150036 issue: 04 year: 2021 ident: 1019_CR18 publication-title: Discr Math Algorithms Appl doi: 10.1142/S1793830921500361 – volume: 33 start-page: 13609 year: 2020 ident: 1019_CR10 publication-title: Adv Neural Inf Process Syst – ident: 1019_CR23 doi: 10.1007/s10878-023-01035-4 – ident: 1019_CR24 doi: 10.1007/978-3-031-46826-1_13 – ident: 1019_CR19 doi: 10.1145/2396761.2396857 – ident: 1019_CR3 – ident: 1019_CR9 doi: 10.1145/2020408.2020479 – ident: 1019_CR22 doi: 10.1007/s10878-022-00965-9 – ident: 1019_CR15 – ident: 1019_CR13 – ident: 1019_CR8 doi: 10.1145/2090236.2090255 – ident: 1019_CR7 doi: 10.1109/ICCV.2007.4408853 – ident: 1019_CR2 doi: 10.1137/1.9781611973730.80 – volume: 42 start-page: 427 year: 2011 ident: 1019_CR12 publication-title: J Artif Intell Res |
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| Title | Group fairness in non-monotone submodular maximization |
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