2 citations · 4 across the 5 of their papers we have counts for
5 papers
Learning Representational Disparities
Pavan Ravishankar, Rushabh Shah, Daniel B. Neill
We propose a fair machine learning algorithm to model interpretable differences between observed and desired human decision-making, with the latter aimed at reducing disparity in a…
Be Intentional About Fairness!: Fairness, Size, and Multiplicity in the Rashomon Set
Gordon Dai, Pavan Ravishankar, Rachel Yuan +2
When selecting a model from a set of equally performant models, how much unfairness can you really reduce? Is it important to be intentional about fairness when choosing among this…
Provable Detection of Propagating Sampling Bias in Prediction Models
Pavan Ravishankar, Qingyu Mo, Edward McFowland +1
With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipel…
A Causal Approach for Unfair Edge Prioritization and Discrimination Removal
Pavan Ravishankar, Pranshu Malviya, Balaraman Ravindran
In budget-constrained settings aimed at mitigating unfairness, like law enforcement, it is essential to prioritize the sources of unfairness before taking measures to mitigate them…
A Causal Linear Model to Quantify Edge Flow and Edge Unfairness for UnfairEdge Prioritization and Discrimination Removal
Pavan Ravishankar, Pranshu Malviya, Balaraman Ravindran
Law enforcement must prioritize sources of unfairness before mitigating their underlying unfairness, considering that they have limited resources. Unlike previous works that only m…