most citedHelp or Hinder? Evaluating the Impact of Fairness Metrics and Algorithms in Visualizations for Consensus Ranking

4 citations · 5 across the 5 of their papers we have counts for

collaborators

5 papers

cs.HC2023

Adaptive Assessment of Visualization Literacy

Yuan Cui, Lily W. Ge, Yiren Ding +3

Visualization literacy is an essential skill for accurately interpreting data to inform critical decisions. Consequently, it is vital to understand the evolution of this ability an…

cs.HC20234 cited

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…

cs.HC2023

Taken By Surprise? Evaluating how Bayesian Weighting Influences Peoples' Takeaways in Map Visualizations

Akim Ndlovu, Hilson Shrestha, Lane T. Harrison

Choropleth maps have been studied and extended in many ways to counteract the many biases that can occur when using them. Two recent techniques, Surprise metrics and Value Suppress…

cs.HC2022

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…

cs.CY20221 cited

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…