1 citations · 1 across the 1 of their papers we have counts for
3 papers
cs.IR2020★ 1 cited
A Note on the Significance Adjustment for FA*IR with Two Protected Groups
Meike Zehlike, Tom Sühr, Carlos Castillo
In this report we provide an improvement of the significance adjustment from the FA*IR algorithm of Zehlike et al., which did not work for very short rankings in combination with a…
cs.LG2020
Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring
Tom Sühr, Sophie Hilgard, Himabindu Lakkaraju
Ranking algorithms are being widely employed in various online hiring platforms including LinkedIn, TaskRabbit, and Fiverr. Prior research has demonstrated that ranking algorithms…
cs.IR2019
FairSearch: A Tool For Fairness in Ranked Search Results
Meike Zehlike, Tom Sühr, Carlos Castillo +1
Ranked search results and recommendations have become the main mechanism by which we find content, products, places, and people online. With hiring, selecting, purchasing, and dati…