ABSTRACT
Consequential decision-making typically incentivizes individuals to behave strategically, tailoring their behavior to the specifics of the decision rule. A long line of work has therefore sought to counteract strategic behavior by designing more conservative decision boundaries in an effort to increase robustness to the effects of strategic covariate shift.
We show that these efforts benefit the institutional decision maker at the expense of the individuals being classified. Introducing a notion of social burden, we prove that any increase in institutional utility necessarily leads to a corresponding increase in social burden. Moreover, we show that the negative externalities of strategic classification can disproportionately harm disadvantaged groups in the population.
Our results highlight that strategy-robustness must be weighed against considerations of social welfare and fairness.
- 2018. Princeton and Stanford are latest universities to drop sat/act writing test. (Jul 2018). https:/www.insidehighered.com/admissions/article/2018/07/09/princeton-and-Stanford-are-latest-universities-drop-satact-writingGoogle Scholar
- Heather Antecol, Kelly Bedard, and Jenna Stearns. forthcoming. Equal but Inequitable: Who Benefits from Gender-Neutral Tenure Clock Stopping Policies?. In American Economic Review.Google Scholar
- Solon Barocas and Andrew D Selbst. 2016. Big Data's Disparate Impact. California Law Review 104, 3 (2016), 671.Google Scholar
- Michael Brückner, Christian Kanzow, and Tobias Scheffer. 2012. Static prediction games for adversarial learning problems. Journal of Machine Learning Research (2012). Google ScholarDigital Library
- Michael Brückner and Tobias Scheffer. 2011. Stackelberg Games for Adversarial Prediction problems. In International Conference on Knowledge Discovery and Data Mining (KDD). Google ScholarDigital Library
- Anthony P Carnevale, Nicole Smith, and Artem Gulish. 2018. Women Can't Win: Despite Making Educational Gains and Pursuing High-Wage Majors, Women Still Earn Less than Men. (2018).Google Scholar
- Max Chafkin. 2016. Confessions of an Instagram Influencer. (Nov 2016). https:/www.bloomberg.com/news/features/2016-11-30/confessions-of-an-instagram-influencerGoogle Scholar
- Alexandra Chouldechova. 2017. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data (2017).Google Scholar
- Danielle Keats Citron and Frank Pasquale. 2014. The Scored Society: Due Process for Automated Predictions. Washington Law Review 89, 1 (2014), 1.Google Scholar
- Nilesh Dalvi, Pedro Domingos, Sumit Sanghai, Deepak Verma, et al. 2004. Adversarial classification. In International Conference on Knowledge Discovery and Data Mining (KDD). Google ScholarDigital Library
- Mary Daly, Bart Hobijn, Joseph H Pedtke, et al. 2017. Disappointing facts about the black-white wage gap. FRBSF Economic Letter 2017 (2017), 26.Google Scholar
- Jinshuo Dong, Aaron Roth, Zachary Schutzman, Bo Waggoner, and Zhiwei Steven Wu. 2018. Strategic Classification from Revealed Preferences. In Conference on Economics and Computation (EC). Google ScholarDigital Library
- Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012. Fairness through awareness. In Innovations in Theoretical Computer Science (ITCS). Google ScholarDigital Library
- Moritz Hardt, Nimrod Megiddo, Christos Papadimitriou, and Mary Wootters. 2016. Strategic classification. In Conference on Innovations in Theoretical Computer Science (ITCS). Google ScholarDigital Library
- Moritz Hardt, Eric Price, Nati Srebro, et al. 2016. Equality of opportunity in supervised learning. In Advances in Neural Information Processing Systems (NIPS). Google ScholarDigital Library
- Caroline Hoxby, Sarah Turner, et al. 2013. Expanding college opportunities for high-achieving, low income students. Stanford Institute for Economic Policy Research Discussion Paper (2013).Google Scholar
- Caroline M Hoxby and Christopher Avery. 2012. The missing "one-offs": The hidden supply of high-achieving, low income students. Technical Report. National Bureau of Economic Research.Google Scholar
- Lily Hu, Nicole Immorlica, and Jennifer Wortman Vaughan. 2019. The Disparate Effects of Strategic Manipulation. In FAT*. Google ScholarDigital Library
- Muhammad Imran, Carlos Castillo, Ji Lucas, Patrick Meier, and Sarah Vieweg. 2014. AIDR: Artificial intelligence for disaster response. In International Conference on World Wide Web (WWW). Google ScholarDigital Library
- Sarah Jeong. 2018. Bad Romance. (July 2018). https:/www.theverge.com/2018/7/16/17566276/cockygate-amazon-kindle-unlimited-algorithm-self-published-romance-novel-cabalGoogle Scholar
- Jon Kleinberg and Manish Raghavan. 2018. How Do Classifiers Induce Agents To Invest Effort Strategically? arXiv preprint arXiv:1807.05307 (2018).Google Scholar
- Haim Levy. 1992. Stochastic dominance and expected utility: survey and analysis. Management science (1992).Google Scholar
- Lydia T Liu, Sarah Dean, Esther Rolf, Max Simchowitz, and Moritz Hardt. 2018. Delayed impact of fair machine learning. arXiv preprint arXiv:1803.04383 (2018).Google Scholar
- Executive Office of the President, Cecilia Munoz, Domestic Policy Council Director, Megan (US Chief Technology Officer Smith (Office of Science, Technology Policy)), DJ (Deputy Chief Technology Officer for Data Policy, Chief Data Scientist Patil (Office of Science, and Technology Policy)). 2016. Big data: A report on algorithmic systems, opportunity, and civil rights. Executive Office of the President.Google Scholar
- US Federal Reserve. 2007. Report to the congress on credit scoring and its effects on the availability and affordability of credit.Google Scholar
- Marilyn Strathern. 1997. Improving ratings': audit in the British University system. European review 5, 3 (1997), 305--321.Google Scholar
Index Terms
- The Social Cost of Strategic Classification
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