Document Open Access Logo

Setting Fair Incentives to Maximize Improvement

Authors Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum, Keziah Naggita

PDF
Thumbnail PDF

File

LIPIcs.FORC.2023.5.pdf
  • Filesize: 0.83 MB
  • 22 pages

Document Identifiers

Author Details

Saba Ahmadi
  • Toyota Technological Institute at Chicago, IL, USA
Hedyeh Beyhaghi
  • Carnegie Mellon University, Pittsburgh, PA, USA
Avrim Blum
  • Toyota Technological Institute at Chicago, IL, USA
Keziah Naggita
  • Toyota Technological Institute at Chicago, IL, USA

Funding

This work was supported in part by the National Science Foundation under grants CCF-1733556 and CCF-1815011 and by the Simons Foundation under the Simons Collaboration on the Theory of Algorithmic Fairness.

Cite AsGet BibTex

Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum, and Keziah Naggita. Setting Fair Incentives to Maximize Improvement. In 4th Symposium on Foundations of Responsible Computing (FORC 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 256, pp. 5:1-5:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.FORC.2023.5

Abstract

We consider the problem of helping agents improve by setting goals. Given a set of target skill levels, we assume each agent will try to improve from their initial skill level to the closest target level within reach (or do nothing if no target level is within reach). We consider two models: the common improvement capacity model, where agents have the same limit on how much they can improve, and the individualized improvement capacity model, where agents have individualized limits. Our goal is to optimize the target levels for social welfare and fairness objectives, where social welfare is defined as the total amount of improvement, and we consider fairness objectives when the agents belong to different underlying populations. We prove algorithmic, learning, and structural results for each model. A key technical challenge of this problem is the non-monotonicity of social welfare in the set of target levels, i.e., adding a new target level may decrease the total amount of improvement; agents who previously tried hard to reach a distant target now have a closer target to reach and hence improve less. This especially presents a challenge when considering multiple groups because optimizing target levels in isolation for each group and outputting the union may result in arbitrarily low improvement for a group, failing the fairness objective. Considering these properties, we provide algorithms for optimal and near-optimal improvement for both social welfare and fairness objectives. These algorithmic results work for both the common and individualized improvement capacity models. Furthermore, despite the non-monotonicity property and interference of the target levels, we show a placement of target levels exists that is approximately optimal for the social welfare of each group. Unlike the algorithmic results, this structural statement only holds in the common improvement capacity model, and we illustrate counterexamples of this result in the individualized improvement capacity model. Finally, we extend our algorithms to learning settings where we have only sample access to the initial skill levels of agents.

Subject Classification

ACM Subject Classification
  • Theory of computation → Algorithmic mechanism design
  • Theory of computation → Machine learning theory
Keywords
  • Algorithmic Fairness
  • Learning for Strategic Behavior
  • Incentivizing Improvement

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

Related Versions

References

  1. Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum, and Keziah Naggita. The strategic perceptron. In Péter Biró, Shuchi Chawla, and Federico Echenique, editors, EC '21: The 22nd ACM Conference on Economics and Computation, Budapest, Hungary, July 18-23, 2021, pages 6-25. ACM, 2021. URL: https://doi.org/10.1145/3465456.3467629.
  2. Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum, and Keziah Naggita. On classification of strategic agents who can both game and improve. In L. Elisa Celis, editor, 3rd Symposium on Foundations of Responsible Computing, FORC 2022, June 6-8, 2022, Cambridge, MA, USA, volume 218 of LIPIcs, pages 3:1-3:22. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022. URL: https://doi.org/10.4230/LIPIcs.FORC.2022.3.
  3. Tal Alon, Magdalen Dobson, Ariel Procaccia, Inbal Talgam-Cohen, and Jamie Tucker-Foltz. Multiagent evaluation mechanisms. In Proceedings of the AAAI Conference on Artificial Intelligence, 34(02):1774-1781, April 2020. URL: https://doi.org/10.1609/aaai.v34i02.5543.
  4. Ashton Anderson, Daniel Huttenlocher, Jon Kleinberg, and Jure Leskovec. Steering user behavior with badges. In Proceedings of the 22nd International Conference on World Wide Web, WWW '13, pages 95-106, New York, NY, USA, 2013. Association for Computing Machinery. URL: https://doi.org/10.1145/2488388.2488398.
  5. Moshe Babaioff, Shahar Dobzinski, Sigal Oren, and Aviv Zohar. On bitcoin and red balloons. In Boi Faltings, Kevin Leyton-Brown, and Panos Ipeirotis, editors, Proceedings of the 13th ACM Conference on Electronic Commerce, EC 2012, Valencia, Spain, June 4-8, 2012, pages 56-73. ACM, 2012. URL: https://doi.org/10.1145/2229012.2229022.
  6. Maria-Florina Balcan, Dan DeBlasio, Travis Dick, Carl Kingsford, Tuomas Sandholm, and Ellen Vitercik. How much data is sufficient to learn high-performing algorithms? generalization guarantees for data-driven algorithm design. In Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing, STOC 2021, pages 919-932, New York, NY, USA, 2021. Association for Computing Machinery. URL: https://doi.org/10.1145/3406325.3451036.
  7. Yahav Bechavod, Katrina Ligett, Zhiwei Steven Wu, and Juba Ziani. Gaming helps! learning from strategic interactions in natural dynamics. In Arindam Banerjee and Kenji Fukumizu, editors, The 24th International Conference on Artificial Intelligence and Statistics, AISTATS 2021, April 13-15, 2021, Virtual Event, volume 130 of Proceedings of Machine Learning Research, pages 1234-1242. PMLR, 2021. URL: http://proceedings.mlr.press/v130/bechavod21a.html.
  8. Yahav Bechavod, Chara Podimata, Zhiwei Steven Wu, and Juba Ziani. Information discrepancy in strategic learning. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvári, Gang Niu, and Sivan Sabato, editors, International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA, volume 162 of Proceedings of Machine Learning Research, pages 1691-1715. PMLR, 2022. URL: https://proceedings.mlr.press/v162/bechavod22a.html.
  9. Mark Braverman and Sumegha Garg. The role of randomness and noise in strategic classification. In Aaron Roth, editor, 1st Symposium on Foundations of Responsible Computing, FORC 2020, June 1-3, 2020, Harvard University, Cambridge, MA, USA (virtual conference), volume 156 of LIPIcs, pages 9:1-9:20. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020. URL: https://doi.org/10.4230/LIPIcs.FORC.2020.9.
  10. Michael Brückner and Tobias Scheffer. Stackelberg games for adversarial prediction problems. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’11, pages 547-555, New York, NY, USA, 2011. Association for Computing Machinery. URL: https://doi.org/10.1145/2020408.2020495.
  11. Moira Burke, Cameron Marlow, and Thomas M. Lento. Feed me: motivating newcomer contribution in social network sites. In Dan R. Olsen Jr., Richard B. Arthur, Ken Hinckley, Meredith Ringel Morris, Scott E. Hudson, and Saul Greenberg, editors, Proceedings of the 27th International Conference on Human Factors in Computing Systems, CHI 2009, Boston, MA, USA, April 4-9, 2009, pages 945-954. ACM, 2009. URL: https://doi.org/10.1145/1518701.1518847.
  12. Moira Burke and Burr Settles. Plugged in to the community: social motivators in online goal-setting groups. In Marcus Foth, Jesper Kjeldskov, and Jeni Paay, editors, Proceedings of the Fifth International Conference on Communities and Technologies, C&T 2011, Brisbane, QLD, Australia, June 29 - July 2, 2011, pages 1-10. ACM, 2011. URL: https://doi.org/10.1145/2103354.2103356.
  13. Emily Diana, Travis Dick, Hadi Elzayn, Michael Kearns, Aaron Roth, Zachary Schutzman, Saeed Sharifi-Malvajerdi, and Juba Ziani. Algorithms and learning for fair portfolio design. In Proceedings of the 22nd ACM Conference on Economics and Computation, EC '21, pages 371-389, New York, NY, USA, 2021. Association for Computing Machinery. URL: https://doi.org/10.1145/3465456.3467646.
  14. Jinshuo Dong, Aaron Roth, Zachary Schutzman, Bo Waggoner, and Zhiwei Steven Wu. Strategic classification from revealed preferences. In Proceedings of the 2018 ACM Conference on Economics and Computation, EC ’18, pages 55-70, New York, NY, USA, 2018. Association for Computing Machinery. URL: https://doi.org/10.1145/3219166.3219193.
  15. David Easley and Arpita Ghosh. Incentives, gamification, and game theory: An economic approach to badge design. In Proceedings of the Fourteenth ACM Conference on Electronic Commerce, EC '13, pages 359-376, New York, NY, USA, 2013. Association for Computing Machinery. URL: https://doi.org/10.1145/2492002.2482571.
  16. Alex Frankel and Navin Kartik. Improving Information from Manipulable Data. Journal of the European Economic Association, 20(1):79-115, June 2021. URL: https://doi.org/10.1093/jeea/jvab017.
  17. Nika Haghtalab, Nicole Immorlica, Brendan Lucier, and Jack Z. Wang. Maximizing welfare with incentive-aware evaluation mechanisms. In Christian Bessiere, editor, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20, pages 160-166. International Joint Conferences on Artificial Intelligence Organization, July 2020. Main track. URL: https://doi.org/10.24963/ijcai.2020/23.
  18. Moritz Hardt, Nimrod Megiddo, Christos Papadimitriou, and Mary Wootters. Strategic classification. In Proceedings of the 2016 ACM Conference on Innovations in Theoretical Computer Science, ITCS ’16, pages 111-122, New York, NY, USA, 2016. Association for Computing Machinery. URL: https://doi.org/10.1145/2840728.2840730.
  19. Keegan Harris, Hoda Heidari, and Zhiwei Steven Wu. Stateful strategic regression. CoRR, abs/2106.03827, 2021. URL: https://arxiv.org/abs/2106.03827.
  20. Lily Hu, Nicole Immorlica, and Jennifer Wortman Vaughan. The disparate effects of strategic manipulation. In Proceedings of the Conference on Fairness, Accountability, and Transparency, FAT* '19, pages 259-268, New York, NY, USA, 2019. ACM. URL: https://doi.org/10.1145/3287560.3287597.
  21. Jon Kleinberg and Manish Raghavan. How do classifiers induce agents to invest effort strategically? In Proceedings of the 2019 ACM Conference on Economics and Computation, EC '19, pages 825-844, New York, NY, USA, 2019. Association for Computing Machinery. URL: https://doi.org/10.1145/3328526.3329584.
  22. John Miller, Smitha Milli, and Moritz Hardt. Strategic classification is causal modeling in disguise. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, volume 119 of Proceedings of Machine Learning Research, pages 6917-6926. PMLR, 2020. URL: http://proceedings.mlr.press/v119/miller20b.html.
  23. Smitha Milli, John Miller, Anca D. Dragan, and Moritz Hardt. The social cost of strategic classification. In Proceedings of the Conference on Fairness, Accountability, and Transparency, FAT* '19, pages 230-239, New York, NY, USA, 2019. Association for Computing Machinery. URL: https://doi.org/10.1145/3287560.3287576.
  24. D. Pollard. Convergence of Stochastic Processes. Springer New York, 1984. URL: https://books.google.com/books?id=B2vgGMa9vd4C.
  25. Yonadav Shavit, Benjamin Edelman, and Brian Axelrod. Learning from strategic agents: Accuracy, improvement, and causality. In Hal Daumé III and Aarti Singh, editors, Proceedings of the 37th International Conference on Machine Learning, volume abs/2002.10066 of Proceedings of Machine Learning Research, pages 8676-8686. PMLR, 13-18 July 2020. URL: http://proceedings.mlr.press/v119/shavit20a.html.
  26. Ravi Sundaram, Anil Vullikanti, Haifeng Xu, and Fan Yao. Pac-learning for strategic classification. In International Conference on Machine Learning, pages 9978-9988. PMLR, 2021. Google Scholar
  27. David P Williamson and David B Shmoys. The design of approximation algorithms. Cambridge university press, 2011. Google Scholar
  28. Shenke Xiao, Zihe Wang, Mengjing Chen, Pingzhong Tang, and Xiwang Yang. Optimal common contract with heterogeneous agents. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05):7309-7316, April 2020. URL: https://doi.org/10.1609/aaai.v34i05.6224.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact