70 citations · 293 across the 33 of their papers we have counts for
5 papers · 1 filter
De-amplifying Bias from Differential Privacy in Language Model Fine-tuning
Sanjari Srivastava, Piotr Mardziel, Zhikhun Zhang +3
Fairness and privacy are two important values machine learning (ML) practitioners often seek to operationalize in models. Fairness aims to reduce model bias for social/demographic…
Smoothed Geometry for Robust Attribution
Zifan Wang, Haofan Wang, Shakul Ramkumar +3
Feature attributions are a popular tool for explaining the behavior of Deep Neural Networks (DNNs), but have recently been shown to be vulnerable to attacks that produce divergent…
Hunting for Discriminatory Proxies in Linear Regression Models
Samuel Yeom, Anupam Datta, Matt Fredrikson
A machine learning model may exhibit discrimination when used to make decisions involving people. One potential cause for such outcomes is that the model uses a statistical proxy f…
Supervising Feature Influence
Shayak Sen, Piotr Mardziel, Anupam Datta +1
Causal influence measures for machine learnt classifiers shed light on the reasons behind classification, and aid in identifying influential input features and revealing their bias…
Influence-Directed Explanations for Deep Convolutional Networks
Klas Leino, Shayak Sen, Anupam Datta +2
We study the problem of explaining a rich class of behavioral properties of deep neural networks. Distinctively, our influence-directed explanations approach this problem by peerin…