3 papers
cs.LG2026
Does Privacy Always Harm Fairness? Data-Dependent Trade-offs via Chernoff Information Neural Estimation
Arjun Nichani, Hsiang Hsu, Chun-Fu +2
Fairness and privacy are two vital pillars of trustworthy machine learning. Despite extensive research on these individual topics, their relationship has received significantly les…
cs.LG2026
When Machine Learning Gets Personal: Evaluating Prediction and Explanation
Louisa Cornelis, Guillermo Bernárdez, Haewon Jeong +1
In high-stakes domains like healthcare, users often expect that sharing personal information with machine learning systems will yield tangible benefits, such as more accurate diagn…
cs.CV2025
Gone With the Bits: Revealing Racial Bias in Low-Rate Neural Compression for Facial Images
Tian Qiu, Arjun Nichani, Rasta Tadayontahmasebi +1
Neural compression methods are gaining popularity due to their superior rate-distortion performance over traditional methods, even at extremely low bitrates below 0.1 bpp. As deep…