Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring

Sühr, Tom; Hilgard, Sophie; Lakkaraju, Himabindu

doi:10.1145/3461702.3462602

Cited by 23 publications

(12 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Gender [35,36,37,38,39,40,41,42,43,44,45,46,47,48,49] • Race [50,40,51,52,49] • Age [35,53,54,55,38] • Nationality [56] Target: Merit-based fairness -attained through a user's merit over time.…”

Section: Notions Of Fairnessmentioning

confidence: 99%

“…• Education [54,57] • Income [54] Target: Behavior-oriented fairness -attained based on a user's engagement with the system/item catalog.…”

Section: Notions Of Fairnessmentioning

confidence: 99%

“…Unfortunately, research that involves humans is very rare. An example for a work that considers the effects of fair rankings can be found in [54], where mixed effects were observed. Another potential issue of the used metrics is that they may be a strong over-simplification or too strong abstraction of the real problems.…”

Section: Category Of Metrics Examplesmentioning

confidence: 99%

See 2 more Smart Citations

Fairness in Recommender Systems: Research Landscape and Future Directions

Deldjoo¹,

Jannach²,

Bellogín³

et al. 2022

Preprint

View full text Add to dashboard Cite

Recommender systems can strongly influence which information we see online, e.g, on social media, and thus impact our beliefs, decisions, and actions. At the same time, these systems can create substantial business value for different stakeholders. Given the growing potential impact of such AI-based systems on individuals, organizations, and society, questions of fairness have gained increased attention in recent years. However, research on fairness in recommender systems is still a developing area. In this survey, we first review the fundamental concepts and notions of fairness that were put forward in the area in the recent past. Afterwards, we provide a survey of how research in this area is currently operationalized, for example, in terms of the general research methodology, fairness metrics, and algorithmic approaches. Overall, our analysis of recent works points to certain research gaps. In particular, we find that in many research works in computer science very abstract problem operationalizations are prevalent, which circumvent the fundamental and important question of what represents a fair recommendation in the context of a given application.

show abstract

Section: Notions Of Fairnessmentioning

confidence: 99%

“…• Education [54,57] • Income [54] Target: Behavior-oriented fairness -attained based on a user's engagement with the system/item catalog.…”

Section: Notions Of Fairnessmentioning

confidence: 99%

See 1 more Smart Citation

Fairness in Recommender Systems: Research Landscape and Future Directions

Deldjoo¹,

Jannach²,

Bellogín³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Finally, algorithmic decision-making systems are biased. Algorithmic systems can only be as good as the corpus of data on which they are based and, as such, data that is biased along with race, gender, sex, and socioeconomic lines will lead to biased results (Bridges, 2017;Caliskan et al, 2017;Katyal, 2019;Noble, 2018;O'Neil, 2016;Sühr et al, 2021). Although it is true that some of these concerns may be ameliorated with design changes, algorithms' deficiencies may threaten the legitimacy of authorities in democratic societies (Berman, 2018).…”

Section: The Functions and Risks Of Algorithmic Decision-makingmentioning

confidence: 99%

Governing algorithmic decisions: The role of decision importance and governance on perceived legitimacy of algorithmic decisions

Waldman

Martin

2022

Big Data & Society

View full text Add to dashboard Cite

The algorithmic accountability literature to date has primarily focused on procedural tools to govern automated decision-making systems. That prescriptive literature elides a fundamentally empirical question: whether and under what circumstances, if any, is the use of algorithmic systems to make public policy decisions perceived as legitimate? The present study begins to answer this question. Using factorial vignette survey methodology, we explore the relative importance of the type of decision, the procedural governance, the input data used, and outcome errors on perceptions of the legitimacy of algorithmic public policy decisions as compared to similar human decisions. Among other findings, we find that the type of decision—low importance versus high importance—impacts the perceived legitimacy of automated decisions. We find that human governance of algorithmic systems (aka human-in-the-loop) increases perceptions of the legitimacy of algorithmic decision-making systems, even when those decisions are likely to result in significant errors. Notably, we also find the penalty to perceived legitimacy is greater when human decision-makers make mistakes than when algorithmic systems make the same errors. The positive impact on perceived legitimacy from governance—such as human-in-the-loop—is greatest for highly pivotal decisions such as parole, policing, and healthcare. After discussing the study’s limitations, we outline avenues for future research.

show abstract

“…The audit literature is inconsistent in whether reliability is included as a concern and, if it is, how it is defined and treated. Specifically, several impactful lines of work do not consider reliability [28,36,40,54,67,71,73]. Of the works that do take reliability under consideration, some refer to this concept as "stability" [10,35,51,63,66], some refer to it as "reliability" [22,44,49,61,66], and some refer to it as "robustness" [15,22,44,46,47].…”

Section: Auditing Of Hiring Algorithmsmentioning

confidence: 99%

An External Stability Audit Framework to Test the Validity of Personality Prediction in AI Hiring

Rhea¹,

Markey²,

D'Arinzo³

et al. 2022

Preprint

View full text Add to dashboard Cite

Automated hiring systems are among the fastest-developing of all high-stakes AI systems. Among these are algorithmic personality tests that use insights from psychometric testing, and promise to surface personality traits indicative of future success based on job seekers' resumes or social media profiles. We interrogate the validity of such systems using stability of the outputs they produce, noting that reliability is a necessary, but not a sufficient,

show abstract

Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring

Cited by 23 publications

References 18 publications

Fairness in Recommender Systems: Research Landscape and Future Directions

Fairness in Recommender Systems: Research Landscape and Future Directions

Governing algorithmic decisions: The role of decision importance and governance on perceived legitimacy of algorithmic decisions

An External Stability Audit Framework to Test the Validity of Personality Prediction in AI Hiring

Contact Info

Product

Resources

About