54 citations · 173 across the 30 of their papers we have counts for
32 papers
On the Power of Randomization in Fair Classification and Representation
Sushant Agarwal, Amit Deshpande
Fair classification and fair representation learning are two important problems in supervised and unsupervised fair machine learning, respectively. Fair classification asks for a c…
Rethinking Robustness of Model Attributions
Sandesh Kamath, Sankalp Mittal, Amit Deshpande +1
For machine learning models to be reliable and trustworthy, their decisions must be interpretable. As these models find increasing use in safety-critical applications, it is import…
Optimizing Group-Fair Plackett-Luce Ranking Models for Relevance and Ex-Post Fairness
Sruthi Gorantla, Eshaan Bhansali, Amit Deshpande +1
In learning-to-rank (LTR), optimizing only the relevance (or the expected ranking utility) can cause representational harm to certain categories of items. Moreover, if there is imp…
Sampling Individually-Fair Rankings that are Always Group Fair
Sruthi Gorantla, Anay Mehrotra, Amit Deshpande +1
Rankings on online platforms help their end-users find the relevant information -- people, news, media, and products -- quickly. Fair ranking tasks, which ask to rank a set of item…
Causal Effect Regularization: Automated Detection and Removal of Spurious Attributes
Abhinav Kumar, Amit Deshpande, Amit Sharma
In many classification datasets, the task labels are spuriously correlated with some input attributes. Classifiers trained on such datasets often rely on these attributes for predi…
On Comparing Fair Classifiers under Data Bias
Mohit Sharma, Amit Deshpande, Rajiv Ratn Shah
In this paper, we consider a theoretical model for injecting data bias, namely, under-representation and label bias (Blum & Stangl, 2019). We empirically study the effect of varyin…