Debiasing Evaluations That are Biased by Evaluations

Wang, Jingyan; Stelmakh, Ivan; Wei, Yuting; Shah, Nihar B.

doi:10.48550/arxiv.2012.00714

Cited by 4 publications

(9 citation statements)

References 41 publications

(63 reference statements)

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“…Nonetheless, there is theoretical work on mitigating bias under various mathematical assumptions. For instance, attempts have been made to address miscalibration of evaluations between multiple evaluators [100], and techniques have been developed for cases where some information is known about how biased each evaluator is in each evaluation [101]. In general, mathematical techniques can be developed as long as some assumptions on bias are made.…”

Section: Biases In Datamentioning

confidence: 99%

Don't let Ricci v. DeStefano Hold You Back: A Bias-Aware Legal Solution to the Hiring Paradox

Salem¹,

Desai²,

Gupta³

2022

Preprint

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Companies that try to address inequality in employment face a hiring paradox. Failing to address workforce imbalance can result in legal sanctions and scrutiny, but proactive measures to address these issues might result in the same legal conflict. Recent run-ins of Microsoft and Wells Fargo with the Labor Department's Office of Federal Contract Compliance Programs (OFCCP) are not isolated and are likely to persist. To add to the confusion, existing scholarship on Ricci v. DeStefano often deems solutions to this paradox impossible. Circumventive practices such as the 4/5ths rule further illustrate tensions between too little action and too much action.In this work, we give a powerful way to solve this hiring paradox that tracks both legal and algorithmic challenges. We unpack the nuances of Ricci v. DeStefano and extend the legal literature arguing that certain algorithmic approaches to employment are allowed by introducing the legal practice of banding to evaluate candidates. We thus show that a biasaware technique can be used to diagnose and mitigate "built-in" headwinds in the employment pipeline. We use the machinery of partially ordered sets to handle the presence of uncertainty in evaluations data. This approach allows us to move away from treating "people as numbers" to treating people as individuals-a property that is sought after by Title VII in the context of employment.

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Section: Biases In Datamentioning

confidence: 99%

Don't let Ricci v. DeStefano Hold You Back: A Bias-Aware Legal Solution to the Hiring Paradox

Salem¹,

Desai²,

Gupta³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…An extended version of this paper is available on arXiv (Wang et al 2020), including more extensive related work, more intuition of our approach, and additional theoretical and experimental results.…”

Section: Our Contributionsmentioning

confidence: 99%

“…Then in Section 4.4, we analyze the cross-validation algorithm. The proofs of all results are in Appendix C of the extended version (Wang et al 2020).…”

Section: Theoretical Guaranteesmentioning

confidence: 99%

Debiasing Evaluations That Are Biased by Evaluations

Wang

Stelmakh

Wei

et al. 2021

AAAI

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It is common to evaluate a set of items by soliciting people to rate them. For example, universities ask students to rate the teaching quality of their instructors, and conference organizers ask authors of submissions to evaluate the quality of the reviews. However, in these applications, students often give a higher rating to a course if they receive higher grades in a course, and authors often give a higher rating to the reviews if their papers are accepted to the conference. In this work, we call these external factors the "outcome" experienced by people, and consider the problem of mitigating these outcome-induced biases in the given ratings when some information about the outcome is available. We formulate the information about the outcome as a known partial ordering on the bias. We propose a debiasing method by solving a regularized optimization problem under this ordering constraint, and also provide a carefully designed cross-validation method that adaptively chooses the appropriate amount of regularization. We provide theoretical guarantees on the performance of our algorithm, as well as experimental evaluations.

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“…The results of the 2016 NeurIPS experiment [20] and other studies of bias in evaluative processes [24,21] have brought to the fore the extent of noisy assessments in conference reviewing. When agents evaluate each other to select a subset of themselves -the peer selection problem -various factors can come into play that hinder accurate assessment, including time pressure and strategic behaviour.…”

Section: Introductionmentioning

confidence: 99%

Peer Selection with Noisy Assessments

Lev,

Mattei,

Turrini

et al. 2021

Preprint

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In the peer selection problem a group of agents must select a subset of themselves as winners for, e.g., peer-reviewed grants or prizes. Here, we take a Condorcet view of this aggregation problem, i.e., that there is a ground-truth ordering over the agents and we wish to select the best set of agents, subject to the noisy assessments of the peers. Given this model, some agents may be unreliable, while others might be selfinterested, attempting to influence the outcome in their favour. In this paper we extend PeerNomination, the most accurate peer reviewing algorithm to date, into WeightedPeerNomination, which is able to handle noisy and inaccurate agents. To do this, we explicitly formulate assessors' reliability weights in a way that does not violate strategyproofness, and use this information to reweight their scores. We show analytically that a weighting scheme can improve the overall accuracy of the selection significantly. Finally, we implement several instances of reweighting methods and show empirically that our methods are robust in the face of noisy assessments.

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Debiasing Evaluations That are Biased by Evaluations

Cited by 4 publications

References 41 publications

Don't let Ricci v. DeStefano Hold You Back: A Bias-Aware Legal Solution to the Hiring Paradox

Don't let Ricci v. DeStefano Hold You Back: A Bias-Aware Legal Solution to the Hiring Paradox

Debiasing Evaluations That Are Biased by Evaluations

Peer Selection with Noisy Assessments

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