175 citations · 372 across the 14 of their papers we have counts for
4 papers · 1 filter
HIVE: Evaluating the Human Interpretability of Visual Explanations
Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy +2
As AI technology is increasingly applied to high-impact, high-risk domains, there have been a number of new methods aimed at making AI models more human interpretable. Despite the…
Understanding and Evaluating Racial Biases in Image Captioning
Dora Zhao, Angelina Wang, Olga Russakovsky
Image captioning is an important task for benchmarking visual reasoning and for enabling accessibility for people with vision impairments. However, as in many machine learning sett…
[Re] Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias
Sunnie S. Y. Kim, Sharon Zhang, Nicole Meister +1
Singh et al. (2020) point out the dangers of contextual bias in visual recognition datasets. They propose two methods, CAM-based and feature-split, that better recognize an object…
Directional Bias Amplification
Angelina Wang, Olga Russakovsky
Mitigating bias in machine learning systems requires refining our understanding of bias propagation pathways: from societal structures to large-scale data to trained models to impa…