58 citations · 124 across the 8 of their papers we have counts for
8 papers
Evergreen: Efficient Claim Verification for Semantic Aggregates
Alexander W. Lee, Benjamin Han, Shayak Sen +3
With recent semantic query processing engines, semantic aggregation has become a primitive operator, enabling the reduction of a relation into a natural language aggregate using an…
Black-Box Audits for Group Distribution Shifts
Marc Juarez, Samuel Yeom, Matt Fredrikson
When a model informs decisions about people, distribution shifts can create undue disparities. However, it is hard for external entities to check for distribution shift, as the mod…
Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness
Samuel Yeom, Matt Fredrikson
We turn the definition of individual fairness on its head---rather than ascertaining the fairness of a model given a predetermined metric, we find a metric for a given model that s…
Learning Fair Representations for Kernel Models
Zilong Tan, Samuel Yeom, Matt Fredrikson +1
Fair representations are a powerful tool for establishing criteria like statistical parity, proxy non-discrimination, and equality of opportunity in learned models. Existing techni…
FlipTest: Fairness Testing via Optimal Transport
Emily Black, Samuel Yeom, Matt Fredrikson
We present FlipTest, a black-box technique for uncovering discrimination in classifiers. FlipTest is motivated by the intuitive question: had an individual been of a different prot…
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…
Avoiding Disparity Amplification under Different Worldviews
Samuel Yeom, Michael Carl Tschantz
We mathematically compare four competing definitions of group-level nondiscrimination: demographic parity, equalized odds, predictive parity, and calibration. Using the theoretical…
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson +1
Machine learning algorithms, when applied to sensitive data, pose a distinct threat to privacy. A growing body of prior work demonstrates that models produced by these algorithms m…