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Paper
Paper
Paper
March
8
22:00
-
23:45
UTC
Add to Calendar 03/08/2021 10:00 PM 03/08/2021 11:45 PM UTC Paper Session 7 Check out this session on the FAccT Hub. https://2021.facctconference.org/conference-agenda/session-7
Track One

Paper Session 7

Session Chair:
Moderator:
Discussant:
Henriette Cramer
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Abstract

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Allocating Opportunities in a Dynamic Model of Intergenerational Mobility

Hoda Heidari, Jon Kleinberg
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Abstract

Opportunities such as higher education can promote intergenerational mobility, leading individuals to achieve levels of socioeconomic status above that of their parents. We develop a dynamic model for allocating such opportunities in a society that exhibits bottlenecks in mobility; the problem of optimal allocation reflects a tradeoff between the benefits conferred by the opportunities in the current generation and the potential to elevate the socioeconomic status of recipients, shaping the composition of future generations in ways that can benefit further from the opportunities. We show how optimal allocations in our model arise as solutions to continuous optimization problems over multiple generations, and we find in general that these optimal solutions can favor recipients of low socioeconomic status over slightly higher-performing individuals of high socioeconomic status — a form of socioeconomic affirmative action that the society in our model discovers in the pursuit of purely payoff-maximizing goals. We characterize how the structure of the model can lead to either temporary or persistent affirmative action, and we consider extensions of the model with more complex processes modulating the movement between different levels of socioeconomic status. 

The Distributive Effects of Risk Prediction in Environmental Compliance: Algorithmic Design, Environmental Justice, and Public Policy

Elinor Benami, Reid Whitaker, Vincent La, Hongjin Lin, Brandon R. Anderson, and Daniel E. Ho
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Abstract

Government agencies are embracing machine learning to support a variety of resource allocation decisions. The U.S. Environmental Protection Agency (EPA), for example, has engaged academic research labs to test the use of machine learning in support of an important national initiative to reduce Clean Water Act violations. We evaluate prototypical risk prediction models that can support compliance interventions and demonstrate how critical algorithmic design choices can generate or mitigate disparate impact in environmental enforcement. First, we show that the definition of which facilities to focus on through this national compliance initiative hinges on arbitrary differences in state-level permitting schemes, causing a shift in environmental protection away from areas with more minority populations. Second, the policy objective to reduce the noncompliance rate is encoded in a classification model, which does not account for the extent of pollution beyond the permitted limit. We hence compare allocation schemes between regression and classification, and show that the latter directs attention towards facilities in more rural and white areas. Overall, our study illustrates that as machine learning enters government, algorithmic design can both embed and elucidate sources of administrative policy discretion with discernable distributional consequences.

The Effect of the Rooney Rule on Implicit Bias in the Long Term

L. Elisa Celis, Chris Hays, Anay Mehrotra, Nisheeth K. Vishnoi
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Abstract

The Rooney Rule, originally proposed to counter implicit bias in hiring, has been implemented in the private and public sector in various settings. This rule requires that a decision-maker include at least one candidate from an underrepresented group in their shortlist of candidates. Recently, [42] proposed a mathematical model of implicit bias and studied the effectiveness of the Rooney Rule when applied to a single selection decision. However, selection decisions often occur repeatedly over time; e.g., a software firm is continuously hiring employees or a university makes admissions decisions every year. Further, it has been observed that, given consistent counterstereotypical feedback, implicit biases against underrepresented candidates can change. In this paper, we propose a model of how a decision-maker's implicit bias changes over time given their hiring decisions either with or without the Rooney Rule in place. Our model draws from the work of [42] and the literature on opinion dynamics. Our main result is that, for this model, when the decision-maker is constrained by the Rooney Rule, their implicit bias roughly reduces at a rate that is inverse of the size of the shortlist---independent of the total number of candidates, whereas without the Rooney Rule, the rate is inversely proportional to the number of candidates. Thus, our model predicts that when the number of candidates is much larger than the size of the shortlist, the Rooney Rule enables a significantly faster reduction in implicit bias, providing additional reason in favor of instating it as a strategy to mitigate implicit bias. Towards empirically evaluating the long-term effect of the Rooney Rule in repeated selection decisions, we conduct an iterative candidate selection experiment on Amazon Mechanical Turk. We observe that, indeed, decision-makers subject to the Rooney Rule select more minority candidates in addition to those required by the rule itself than they would if no rule is in effect, and in fact are able to do so without considerably decreasing the utility of candidates selected.

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