24 citations · 32 across the 3 of their papers we have counts for
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cs.LG2024
Learning Recourse Costs from Pairwise Feature Comparisons
Kaivalya Rawal, Himabindu Lakkaraju
This paper presents a novel technique for incorporating user input when learning and inferring user preferences. When trying to provide users of black-box machine learning models w…
cs.LG2020★ 8 cited
Algorithmic Recourse in the Wild: Understanding the Impact of Data and Model Shifts
Kaivalya Rawal, Ece Kamar, Himabindu Lakkaraju
As predictive models are increasingly being deployed to make a variety of consequential decisions, there is a growing emphasis on designing algorithms that can provide recourse to…
cs.LG2020★ 24 cited
Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses
Kaivalya Rawal, Himabindu Lakkaraju
As predictive models are increasingly being deployed in high-stakes decision-making, there has been a lot of interest in developing algorithms which can provide recourses to affect…