4 citations · 5 across the 4 of their papers we have counts for
4 papers
Hidden or Inferred: Fair Learning-To-Rank with Unknown Demographics
Oluseun Olulana, Kathleen Cachel, Fabricio Murai +1
As learning-to-rank models are increasingly deployed for decision-making in areas with profound life implications, the FairML community has been developing fair learning-to-rank (L…
Help or Hinder? Evaluating the Impact of Fairness Metrics and Algorithms in Visualizations for Consensus Ranking
Hilson Shrestha, Kathleen Cachel, Mallak Alkhathlan +2
For applications where multiple stakeholders provide recommendations, a fair consensus ranking must not only ensure that the preferences of rankers are well represented, but must a…
FairFuse: Interactive Visual Support for Fair Consensus Ranking
Hilson Shrestha, Kathleen Cachel, Mallak Alkhathlan +2
Fair consensus building combines the preferences of multiple rankers into a single consensus ranking, while ensuring any group defined by a protected attribute (such as race or gen…
MANI-Rank: Multiple Attribute and Intersectional Group Fairness for Consensus Ranking
Kathleen Cachel, Elke Rundensteiner, Lane Harrison
Combining the preferences of many rankers into one single consensus ranking is critical for consequential applications from hiring and admissions to lending. While group fairness h…