7 citations · 26 across the 12 of their papers we have counts for
6 papers · 1 filter
Feature-Wise Bias Amplification
Klas Leino, Emily Black, Matt Fredrikson +2
We study the phenomenon of bias amplification in classifiers, wherein a machine learning model learns to predict classes with a greater disparity than the underlying ground truth.…
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…
Contextual and Granular Policy Enforcement in Database-backed Applications
Abhishek Bichhawat, Matt Fredrikson, Jean Yang +1
Database-backed applications rely on inlined policy checks to process users' private and confidential data in a policy-compliant manner as traditional database access control mecha…
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…
Verifying and Synthesizing Constant-Resource Implementations with Types
Van Chan Ngo, Mario Dehesa-Azuara, Matthew Fredrikson +1
We propose a novel type system for verifying that programs correctly implement constant-resource behavior. Our type system extends recent work on automatic amortized resource analy…