5 papers · 1 filter
Understanding and evaluating computer vision models through the lens of counterfactuals
Pushkar Shukla
Counterfactual reasoning -- the practice of asking ``what if'' by varying inputs and observing changes in model behavior -- has become central to interpretable and fair AI. This th…
Mitigate One, Skew Another? Tackling Intersectional Biases in Text-to-Image Models
Pushkar Shukla, Aditya Chinchure, Emily Diana +5
The biases exhibited by text-to-image (TTI) models are often treated as independent, though in reality, they may be deeply interrelated. Addressing bias along one dimension - such…
BiasConnect: Investigating Bias Interactions in Text-to-Image Models
Pushkar Shukla, Aditya Chinchure, Emily Diana +5
The biases exhibited by Text-to-Image (TTI) models are often treated as if they are independent, but in reality, they may be deeply interrelated. Addressing bias along one dimensio…
TIBET: Identifying and Evaluating Biases in Text-to-Image Generative Models
Aditya Chinchure, Pushkar Shukla, Gaurav Bhatt +4
Text-to-Image (TTI) generative models have shown great progress in the past few years in terms of their ability to generate complex and high-quality imagery. At the same time, thes…
Utilizing Adversarial Examples for Bias Mitigation and Accuracy Enhancement
Pushkar Shukla, Dhruv Srikanth, Lee Cohen +1
We propose a novel approach to mitigate biases in computer vision models by utilizing counterfactual generation and fine-tuning. While counterfactuals have been used to analyze and…