Closing the GAP: Group-Aware Parallelization for Online Selection of Candidates with Biased Evaluations

Salem, Jad; Gupta, Swati

doi:10.2139/ssrn.3444283

Cited by 7 publications

(13 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to implement the poset approach, this screening process must be adapted to take a partial ranking as input instead of numeric scores or a total ranking. Typically, this can be done by prioritizing maximality and randomizing wherever incomparabilities necessitate (see [90] for an example of this in an online setting).…”

Section: A New Approach: Coping With Uncertainty Using Partial Ordersmentioning

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: A New Approach: Coping With Uncertainty Using Partial Ordersmentioning

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…Manshadi et al [39] studied fair online rationing such that each arriving agent can receive a fair share of resources proportional to its demand. The fairness issue has been studied in other domains/applications as well, see, e.g., online selection of candidates [40], influence maximization [41], banditbased online learning [42][43][44], online resource allocation [45,46], and classification [47].…”

Section: Other Related Workmentioning

confidence: 99%

Fairness Maximization among Offline Agents in Online-Matching Markets

Ma¹,

Xu²,

Xu³

2021

Preprint

View full text Add to dashboard Cite

Matching markets involve heterogeneous agents (typically from two parties) who are paired for mutual benefit. During the last decade, matching markets have emerged and grown rapidly through the medium of the Internet. They have evolved into a new format, called Online Matching Markets (OMMs), with examples ranging from crowdsourcing to online recommendations to ridesharing. There are two features distinguishing OMMs from traditional matching markets. One is the dynamic arrival of one side of the market: we refer to these as online agents while the rest are offline agents. Examples of online and offline agents include keywords (online) and sponsors (offline) in Google Advertising; workers (online) and tasks (offline) in Amazon Mechanical Turk (AMT); riders (online) and drivers (offline when restricted to a short time window) in ridesharing. The second distinguishing feature of OMMs is the real-time decision-making element. However, studies have shown that the algorithms making decisions in these OMMs leave disparities in the match rates of offline agents. For example, tasks in neighborhoods of low socioeconomic status rarely get matched to gig workers, and drivers of certain races/genders get discriminated against in matchmaking. In this paper, we propose online matching algorithms which optimize for either individual or group-level fairness among offline agents in OMMs. We present two linear-programming (LP) based sampling algorithms, which achieve online competitive ratios at least 0.725 for individual fairness maximization (IFM) and 0.719 for group fairness maximization (GFM), respectively. There are two key ideas helping us break the barrier of 1 − 1/e. One is boosting, which is to adaptively re-distribute all sampling probabilities only among those offline available neighbors for every arriving online agent. The other is attenuation, which aims to balance the matching probabilities among offline agents with different mass allocated by the benchmark LP. We conduct extensive numerical experiments and results show that our boosted version of sampling algorithms are not only conceptually easy to implement but also highly effective in practical instances of fairness-maximization-related models.

show abstract

“…Of course, a highly ranked applicant may nevertheless turn down a job offer. Although we consider the rank of a candidate as an absolute metric of their capacities, in reality, resume screening may suffer from different sources of bias (Salem and Gupta, 2019), but addressing this goes beyond our scope. See also Smith (1975); Tamaki (1991); Vanderbei (2012) for classical treatments.…”

Section: Human Resource Managementmentioning

confidence: 99%

Robust Online Selection with Uncertain Offer Acceptance

Perez-Salazar¹,

Singh²,

Toriello³

2021

Preprint

View full text Add to dashboard Cite

Online advertising has motivated interest in online selection problems. Displaying ads to the right users benefits both the platform (e.g., via pay-per-click) and the advertisers (by increasing their reach). In practice, not all users click on displayed ads, while the platform's algorithm may miss the users most disposed to do so. This mismatch decreases the platform's revenue and the advertiser's chances to reach the right customers. With this motivation, we propose a secretary problem where a candidate may or may not accept an offer according to a known probability p. Because we do not know the top candidate willing to accept an offer, the goal is to maximize a robust objective defined as the minimum over integers k of the probability of choosing one of the top k candidates, given that one of these candidates will accept an offer.Using Markov decision process theory, we derive a linear program for this max-min objective whose solution encodes an optimal policy. The derivation may be of independent interest, as it is generalizable and can be used to obtain linear programs for many online selection models.

show abstract

Closing the GAP: Group-Aware Parallelization for Online Selection of Candidates with Biased Evaluations

Cited by 7 publications

References 26 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

Fairness Maximization among Offline Agents in Online-Matching Markets

Robust Online Selection with Uncertain Offer Acceptance

Contact Info

Product

Resources

About