24 citations · 32 across the 3 of their papers we have counts for
4 papers
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…
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…
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…
Maximizing Contrasting Opinions in Signed Social Networks
Kaivalya Rawal, Arijit Khan
The classic influence maximization problem finds a limited number of influential seed users in a social network such that the expected number of influenced users in the network, fo…