626 citations · 2.7k across the 64 of their papers we have counts for
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Taking Advice from (Dis)Similar Machines: The Impact of Human-Machine Similarity on Machine-Assisted Decision-Making
Nina Grgić-Hlača, Claude Castelluccia, Krishna P. Gummadi
Machine learning algorithms are increasingly used to assist human decision-making. When the goal of machine assistance is to improve the accuracy of human decisions, it might seem…
Measuring Representational Robustness of Neural Networks Through Shared Invariances
Vedant Nanda, Till Speicher, Camila Kolling +3
A major challenge in studying robustness in deep learning is defining the set of ``meaningless'' perturbations to which a given Neural Network (NN) should be invariant. Most work o…
Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Making
Miriam Rateike, Ayan Majumdar, Olga Mineeva +2
Decision making algorithms, in practice, are often trained on data that exhibits a variety of biases. Decision-makers often aim to take decisions based on some ground-truth target…
Scheduling Virtual Conferences Fairly: Achieving Equitable Participant and Speaker Satisfaction
Gourab K. Patro, Prithwish Jana, Abhijnan Chakraborty +2
Recently, almost all conferences have moved to virtual mode due to the pandemic-induced restrictions on travel and social gathering. Contrary to in-person conferences, virtual conf…
FaiRIR: Mitigating Exposure Bias from Related Item Recommendations in Two-Sided Platforms
Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh +2
Related Item Recommendations (RIRs) are ubiquitous in most online platforms today, including e-commerce and content streaming sites. These recommendations not only help users compa…
Alexa, in you, I trust! Fairness and Interpretability Issues in E-commerce Search through Smart Speakers
Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh +2
In traditional (desktop) e-commerce search, a customer issues a specific query and the system returns a ranked list of products in order of relevance to the query. An increasingly…