Designing Fair Ranking Schemes

Asudeh, Abolfazl; Jagadish, H. V.; Stoyanovich, Julia; Das, Gautam

doi:10.1145/3299869.3300079

Cited by 137 publications

(96 citation statements)

References 38 publications

(63 reference statements)

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“…[12]). • Modifying the training process to generate a bias-free model (e.g., [5]). • Post-processing includes the modification of the results of a trained machine learning model, using techniques such as calibration of regression or classification output and reranking of results (e.g., [42]).…”

Section: Lessons Learned In Practicementioning

confidence: 99%

“…A method to assist the algorithm designer to generate a fair linear ranking model has been proposed in [5]. With respect to a fixed set of items, given a weight vector for ranking, the method in [5] computes a similar vector that meets fairness requirements. This approach is not applicable in our setting since it assumes that the candidate set of items to be ranked is fixed, whereas this set depends on the query in our case.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search

Geyik

Ambler

Kenthapadi

2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

260

106

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We present a framework for quantifying and mitigating algorithmic bias in mechanisms designed for ranking individuals, typically used as part of web-scale search and recommendation systems. We first propose complementary measures to quantify bias with respect to protected attributes such as gender and age. We then present algorithms for computing fairness-aware re-ranking of results. For a given search or recommendation task, our algorithms seek to achieve a desired distribution of top ranked results with respect to one or more protected attributes. We show that such a framework can be tailored to achieve fairness criteria such as equality of opportunity and demographic parity depending on the choice of the desired distribution. We evaluate the proposed algorithms via extensive simulations over different parameter choices, and study the effect of fairness-aware ranking on both bias and utility measures. We finally present the online A/B testing results from applying our framework towards representative ranking in LinkedIn Talent Search, and discuss the lessons learned in practice. Our approach resulted in tremendous improvement in the fairness metrics (nearly three fold increase in the number of search queries with representative results) without affecting the business metrics, which paved the way for deployment to 100% of LinkedIn Recruiter users worldwide. Ours is the first large-scale deployed framework for ensuring fairness in the hiring domain, with the potential positive impact for more than 630M LinkedIn members.

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Section: Lessons Learned In Practicementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search

Geyik

Ambler

Kenthapadi

2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

260

106

View full text Add to dashboard Cite

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“…In fair ranking, early studies mainly focused on quantifying the discrimination with proposed ranking measures in the top-k list [32], or indirect discrimination [43]. Recently, fair ranking methods have been proposed [2,41,23]. Asudeh et al [2] scored items based on a set of desired attribute weights to achieve fairness.…”

Section: Fair Rankingmentioning

confidence: 99%

“…Recently, fair ranking methods have been proposed [2,41,23]. Asudeh et al [2] scored items based on a set of desired attribute weights to achieve fairness. On the other hand, Karako and…”

Section: Fair Rankingmentioning

confidence: 99%

FairSight: Visual Analytics for Fairness in Decision Making

Ahn

Lin

2019

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. We propose a design framework to protect individuals and groups from discrimination in algorithm-assisted decision making. A visual analytic system, FairSight, is implemented based on our proposed framework, to help data scientists and practitioners make fair decisions. The decision is made through ranking individuals who are either members of a protected group (orange bars) or a non-protected group (green bars). (a) The system provides a pipeline to help users understand the possible bias in a machine learning task as a mapping from the input space to the output space. (b) Different notions of fairnessindividual fairness and group fairnessare measured and summarized numerically and visually. For example, the individual fairness is quantified by how pairwise distances between individuals are preserved through the mapping. The group fairness is quantified by the extent to which it leads to fair outcome distribution across groups, with (i) a 2D plot, (ii) a color-coded matrix , and (iii) a ranked-list plot capturing the pattern of potential biases.The system provides diagnostic modules to help (iv) identify and (v) mitigate biases through (c) investigating features before running a model, and (d) leveraging fairness-aware algorithms during and after the training step.Abstract-Data-driven decision making related to individuals has become increasingly pervasive, but the issue concerning the potential discrimination has been raised by recent studies. In response, researchers have made efforts to propose and implement fairness measures and algorithms, but those efforts have not been translated to the real-world practice of data-driven decision making. As such, there is still an urgent need to create a viable tool to facilitate fair decision making. We propose FairSight, a visual analytic system to address this need; it is designed to achieve different notions of fairness in ranking decisions through identifying the required actions -understanding, measuring, diagnosing and mitigating biases -that together lead to fairer decision making. Through a case study and user study, we demonstrate that the proposed visual analytic and diagnostic modules in the system are effective in understanding the fairness-aware decision pipeline and obtaining more fair outcomes.

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“…Since both lines intersect with the ray of f at the same point, f assigns an equal score to ti and tj. We refer to this function (and its ray) as the ordering exchange (first defined in [13]) between ti and tj, and denote it ×t i ,t j . The ordering between ti and tj changes on two sides of ×t i ,t j : ti is ranked higher than tj one side of the ray, and tj is ranked higher than ti on the other side.…”

Section: Two Dimensional (2d) Rankingmentioning

confidence: 99%

On obtaining stable rankings

et al. 2018

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Decision making is challenging when there is more than one criterion to consider. In such cases, it is common to assign a goodness score to each item as a weighted sum of its attribute values and rank them accordingly. Clearly, the ranking obtained depends on the weights used for this summation. Ideally, one would want the ranked order not to change if the weights are changed slightly. We call this property stability of the ranking. A consumer of a ranked list may trust the ranking more if it has high stability. A producer of a ranked list prefers to choose weights that result in a stable ranking, both to earn the trust of potential consumers and because a stable ranking is intrinsically likely to be more meaningful.In this paper, we develop a framework that can be used to assess the stability of a provided ranking and to obtain a stable ranking within an "acceptable" range of weight values (called "the region of interest"). We address the case where the user cares about the rank order of the entire set of items, and also the case where the user cares only about the top-k items. Using a geometric interpretation, we propose algorithms that produce stable rankings. In addition to theoretical analyses, we conduct extensive experiments on real datasets that validate our proposal.

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Designing Fair Ranking Schemes

Cited by 137 publications

References 38 publications

Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search

Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search

FairSight: Visual Analytics for Fairness in Decision Making

On obtaining stable rankings

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