42 citations · 70 across the 8 of their papers we have counts for
4 papers · 1 filter
OASIS Uncovers: High-Quality T2I Models, Same Old Stereotypes
Sepehr Dehdashtian, Gautam Sreekumar, Vishnu Naresh Boddeti
Images generated by text-to-image (T2I) models often exhibit visual biases and stereotypes of concepts such as culture and profession. Existing quantitative measures of stereotypes…
Fairness and Bias Mitigation in Computer Vision: A Survey
Sepehr Dehdashtian, Ruozhen He, Yi Li +4
Computer vision systems have witnessed rapid progress over the past two decades due to multiple advances in the field. As these systems are increasingly being deployed in high-stak…
Utility-Fairness Trade-Offs and How to Find Them
Sepehr Dehdashtian, Bashir Sadeghi, Vishnu Naresh Boddeti
When building classification systems with demographic fairness considerations, there are two objectives to satisfy: 1) maximizing utility for the specific task and 2) ensuring fair…
FairerCLIP: Debiasing CLIP's Zero-Shot Predictions using Functions in RKHSs
Sepehr Dehdashtian, Lan Wang, Vishnu Naresh Boddeti
Large pre-trained vision-language models such as CLIP provide compact and general-purpose representations of text and images that are demonstrably effective across multiple downstr…