25 citations · 26 across the 2 of their papers we have counts for
4 papers
Unifying Model Explainability and Robustness via Machine-Checkable Concepts
Vedant Nanda, Till Speicher, John P. Dickerson +2
As deep neural networks (DNNs) get adopted in an ever-increasing number of applications, explainability has emerged as a crucial desideratum for these models. In many real-world ta…
Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning
Vedant Nanda, Samuel Dooley, Sahil Singla +2
Deep neural networks (DNNs) are increasingly used in real-world applications (e.g. facial recognition). This has resulted in concerns about the fairness of decisions made by these…
Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours
Vedant Nanda, Pan Xu, Karthik Abinav Sankararaman +2
Rideshare platforms, when assigning requests to drivers, tend to maximize profit for the system and/or minimize waiting time for riders. Such platforms can exacerbate biases that d…
On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning
Hoda Heidari, Vedant Nanda, Krishna P. Gummadi
Most existing notions of algorithmic fairness are one-shot: they ensure some form of allocative equality at the time of decision making, but do not account for the adverse impact o…