1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.CV2023★ 1 cited
Benchmarking Algorithmic Bias in Face Recognition: An Experimental Approach Using Synthetic Faces and Human Evaluation
Hao Liang, Pietro Perona, Guha Balakrishnan
We propose an experimental method for measuring bias in face recognition systems. Existing methods to measure bias depend on benchmark datasets that are collected in the wild and a…
eess.IV2023
Visualizing chest X-ray dataset biases using GANs
Hao Liang, Kevin Ni, Guha Balakrishnan
Recent work demonstrates that images from various chest X-ray datasets contain visual features that are strongly correlated with protected demographic attributes like race and gend…
eess.IV2023
X-ray Recognition: Patient identification from X-rays using a contrastive objective
Hao Liang, Kevin Ni, Guha Balakrishnan
Recent research demonstrates that deep learning models are capable of precisely extracting bio-information (e.g. race, gender and age) from patients' Chest X-Rays (CXRs). In this p…