An Economic Approach to Regulating Algorithms

Rambachan, Ashesh; Kleinberg, Jon; Mullainathan, Sendhil; Ludwig, Jens

doi:10.2139/ssrn.3597843

Cited by 9 publications

(13 citation statements)

References 34 publications

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“…Fairness is a rising concern in economic applications, see, e.g., Cowgill and Tucker (2019), Kleinberg et al (2018), Rambachan et al (2020), Rambachan and Roth (2019). The authors provide fundamental economic insights on the characteristics of optimal decision rules in the presence of discrimination bias.…”

Section: Related Literaturementioning

confidence: 99%

“…An alternative approach consists in choosing appropriately the weights in the above expression to reflect the relative importance of each group to the social planner. For instance we may consider maximizing (Rambachan et al, 2020) arg max…”

Section: Pareto Principle For Treatment Rulesmentioning

confidence: 99%

“…We identify the Pareto frontier as the set of maximizers over any weighted average of the welfares of each group. Therefore, such an approach embeds as a special case maximizing a weighted combination of welfares of each sensitive group (Kitagawa and Tetenov, 2018;Rambachan et al, 2020). However, the weights are not chosen a-priori, but instead, they are part of the decision problem, and directly selected to maximize fairness.…”

Section: Introductionmentioning

confidence: 99%

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Fair Policy Targeting

Viviano¹,

Bradić²

2020

Preprint

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One of the major concerns of targeting interventions on individuals in social welfare programs is discrimination: individualized treatments may induce disparities on sensitive attributes such as age, gender, or race. This paper addresses the question of the design of fair and efficient treatment allocation rules. We adopt the non-maleficence perspective of "first do no harm": we propose to select the fairest allocation within the Pareto frontier. We provide envy-freeness justifications to novel counterfactual notions of fairness. We discuss easy-to-implement estimators of the policy function, by casting the optimization into a mixed-integer linear program formulation. We derive regret bounds on the unfairness of the estimated policy function, and small sample guarantees on the Pareto frontier. Finally, we illustrate our method using an application from education economics.

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Section: Related Literaturementioning

confidence: 99%

Section: Pareto Principle For Treatment Rulesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fair Policy Targeting

Viviano¹,

Bradić²

2020

Preprint

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“…We therefore support the growing evidence in economics and computer science showing that blind algorithms are a misguided approach to fair prediction. Within this literature, [12,7,15] produce theoretical results demonstrating that fairness considerations are best imposed after estimating the most accurate predictions, rather than before, and [11,13] demonstrate this principle empirically in real-world settings.…”

Section: Introductionmentioning

confidence: 99%

Resolving the Disparate Impact of Uncertainty: Affirmative Action vs. Affirmative Information

Reich¹

2021

Preprint

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Algorithmic risk assessments hold the promise of greatly advancing accurate decision-making, but in practice, multiple real-world examples have been shown to distribute errors disproportionately across demographic groups. In this paper, we characterize why error disparities arise in the first place. We show that predictive uncertainty often leads classifiers to systematically disadvantage groups with lowermean outcomes, assigning them smaller true and false positive rates than their higher-mean counterparts. This can occur even when prediction is group-blind. We prove that to avoid these error imbalances, individuals in lower-mean groups must either be over-represented among positive classifications or be assigned more accurate predictions than those in higher-mean groups. We focus on the latter condition as a solution to bridge error rate divides and show that data acquisition for low-mean groups can increase access to opportunity. We call the strategy "affirmative information" and compare it to traditional affirmative action in the classification task of identifying creditworthy borrowers.1 I am grateful to my advisors Anna Mikusheva, Iván Werning, and David Autor for sharing invaluable insights at each stage of the project. I also thank Tom Brennan,

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“…On the question of algorithms in particular, one concern is that the algorithm design problem may be susceptible to bias or induce unwanted discrimination when implemented, relative to rationality. See Rambachan, Kleinberg, Mullainathan, and Ludwig (2020) for an analysis of these issues and how they may be overcome. Syrgkanis, and Tardos (2015) assume players in a repeated auction use a no-regret learning algorithm, making similar behavioral assumptions as we do here.…”

mentioning

confidence: 99%

Machine Learning for Strategic Inference

Cho¹,

Libgober²

2021

Preprint

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We study interactions between strategic players and markets whose behavior is guided by an algorithm. Algorithms use data from prior interactions and a limited set of decision rules to prescribe actions. While as-if rational play need not emerge if the algorithm is constrained, it is possible to guide behavior across a rich set of possible environments using limited details. Provided a condition known as weak learnability holds, Adaptive Boosting algorithms can be specified to induce behavior that is (approximately) as-if rational. Our analysis provides a statistical perspective on the study of endogenous model misspecification.

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An Economic Approach to Regulating Algorithms

Cited by 9 publications

References 34 publications

Fair Policy Targeting

Fair Policy Targeting

Resolving the Disparate Impact of Uncertainty: Affirmative Action vs. Affirmative Information

Machine Learning for Strategic Inference

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