Addressing biases in computer vision models is crucial for real-world AI deployments.
However mitigating visual biases is challenging due to their unexplainable nature often …

The field of computer vision is now a multi-billion dollar enterprise, with its use in
surveillance applications driving this large market share. In the last six years, computer …

Machine learning (ML) models can fail in unexpected ways in the real world, but not all
model failures are equal. With finite time and resources, ML practitioners are forced to …

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 …

We introduce a framework to measure how biases change before and after fine-tuning a
large scale visual recognition model for a downstream task. Deep learning models trained …

Data slice finding is an emerging technique for validating machine learning (ML) models by
identifying and analyzing subgroups in a dataset that exhibit poor performance, often …

Empirical research in science and the humanities is vulnerable to bias which, by definition,
implies incorrect or misleading findings. Artificial intelligence-based analysis of visual …

Many visualization techniques have been created to explain the behavior of computer vision
models but they largely consist of static diagrams that convey limited information. Interactive …

As we introduce complex algorithmic systems into decision-making in high-stakes domains,
system designers need principled approaches to help people set their expectations of these …

Empirical research in science and the humanities is vulnerable to bias which, by definition,
implies incorrect or misleading findings. Artificial intelligence-based analysis of visual …