activity
20212026
most citedComparing Human and Machine Bias in Face Recognition

6 citations · 9 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2026

ANCHOR: Audio-Visually Grounded Chain-of-Thought Reasoning Benchmark

Joel Julin, Souraja Kundu, Liza Dahiya +6

Multimodal large language models (MLLMs) perform well on audio-visual question answering, but correct answers do not necessarily imply that their stated reasoning is grounded in re…

cs.MM2025

MAVERIX: Multimodal Audio-Visual Evaluation and Recognition IndeX

Liuyue Xie, Avik Kuthiala, George Z. Wei +12

We introduce MAVERIX (Multimodal audiovisual Evaluation and Recognition IndeX), a unified benchmark to probe the video understanding in multimodal LLMs, encompassing video, audio,…

cs.CY2022★ 2 cited

Robustness Disparities in Face Detection

Samuel Dooley, George Z. Wei, Tom Goldstein +1

Facial analysis systems have been deployed by large companies and critiqued by scholars and activists for the past decade. Many existing algorithmic audits examine the performance…

cs.CV2022★ 1 cited

Are Commercial Face Detection Models as Biased as Academic Models?

Samuel Dooley, George Z. Wei, Tom Goldstein +1

As facial recognition systems are deployed more widely, scholars and activists have studied their biases and harms. Audits are commonly used to accomplish this and compare the algo…

cs.CV2021★ 6 cited

Comparing Human and Machine Bias in Face Recognition

Samuel Dooley, Ryan Downing, George Wei +10

Much recent research has uncovered and discussed serious concerns of bias in facial analysis technologies, finding performance disparities between groups of people based on perceiv…