Jon Kleinberg
Sendhil Mullainathan
ABSTRACT
Algorithms can be a powerful aid to decision-making - particularly when decisions rely, even implicitly, on predictions [7]. We are already seeing algorithms play this role in domains including hiring, education, lending, medicine, and criminal justice [2, 6, 10]. As is typical in machine learning applications, accuracy is an important measure for these tasks.
Supplemental Material
- Solon Barocas and Andrew Selbst. 2016. Big data's disparate impact. California Law Review, Vol. 104 (2016).Google Scholar
- A. Chouldechova, D. Prado, O. Fialko, and R. Vaithianathan. 2018. A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. In Conference on Fairness, Accountability Transparency (FAT*) .Google Scholar
- Sam Corbett-Davies and Sharad Goel. 2018. The Measure and Mismeasure of Fairness. arxiv.org/1808.00023.Google Scholar
- Finale Doshi-Velez and Been Kim. 2017. A Roadmap for a Rigorous Science of Interpretability. arxiv.org/1702.08608.Google Scholar
- Hanming Fang and Andrea Moro. 2011. Theories of statistical discrimination and affirmative action: A survey. In Handbook of social economics . Vol. 1. Elsevier, 133--200.Google Scholar
- Jon Kleinberg, Hima Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan. 2018. Human Decisions and Machine Predictions. Quarterly Journal of Economics, Vol. 133, 1 (2018), 237--293.Google Scholar
- Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan, and Ziad Obermeyer. 2015. Prediction policy problems. American Economic Review, Vol. 105, 5 (2015), 491--95.Google ScholarCross Ref
- Jon Kleinberg and Sendhil Mullainathan. 2018. Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability. arxiv.org/1809.04578.Google Scholar
- Zachary C. Lipton. 2018. The mythos of model interpretability. Commun. ACM, Vol. 61, 10 (2018), 36--43. Google ScholarDigital Library
- Jonah E. Rockoff, Brian A. Jacob, Thomas J. Kane, and Douglas O. Staiger. 2011. Can you recognize an effective teacher when you recruit one? Education Finance and Policy, Vol. 6, 1 (2011), 43--74.Google ScholarCross Ref
Index Terms
- Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability
Recommendations
Simplicity creates inequity: implications for fairness, stereotypes, and interpretability (invited paper)
STOC 2021: Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of ComputingAlgorithms are increasingly used to aid, or in some cases supplant, human decision-making, particularly for decisions that hinge on predictions. As a result, two additional features in addition to prediction quality have generated interest: (i) to ...
Youth Trust in Social Media Companies and Expectations of Justice: Accountability and Repair After Online Harassment
CSCWSocial media platforms aspire to deliver fair resolutions after online harassment. Platforms rely on sanctions like removing content or banning users but these punitive responses provide little opportunity for justice or reparation for targets of ...
Relay-volunteered multi-rate cooperative MAC protocol for IEEE 802.11 WLANs
In IEEE 802.11, the rate of a station (STA) is dynamically determined by link adaptation. Low-rate STAs tend to hog more channel time than high-rate STAs due to fair characteristics of carrier sense multiple access/collision avoidance, leading to ...
Comments