352 citations · 708 across the 11 of their papers we have counts for
11 papers
Fairness-Aware Online Personalization
G Roshan Lal, Sahin Cem Geyik, Krishnaram Kenthapadi
Decision making in crucial applications such as lending, hiring, and college admissions has witnessed increasing use of algorithmic models and techniques as a result of a confluenc…
Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
Sahin Cem Geyik, Stuart Ambler, Krishnaram Kenthapadi
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…
What's in a Name? Reducing Bias in Bios without Access to Protected Attributes
Alexey Romanov, Maria De-Arteaga, Hanna Wallach +7
There is a growing body of work that proposes methods for mitigating bias in machine learning systems. These methods typically rely on access to protected attributes such as race,…
Entity Personalized Talent Search Models with Tree Interaction Features
Cagri Ozcaglar, Sahin Geyik, Brian Schmitz +4
Talent Search systems aim to recommend potential candidates who are a good match to the hiring needs of a recruiter expressed in terms of the recruiter's search query or job postin…
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting
Maria De-Arteaga, Alexey Romanov, Hanna Wallach +6
We present a large-scale study of gender bias in occupation classification, a task where the use of machine learning may lead to negative outcomes on peoples' lives. We analyze the…
In-Session Personalization for Talent Search
Sahin Cem Geyik, Vijay Dialani, Meng Meng +1
Previous efforts in recommendation of candidates for talent search followed the general pattern of receiving an initial search criteria and generating a set of candidates utilizing…