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 …

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 …

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 …

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 help explain the behavior of
convolutional neural networks (CNNs), but they largely consist of static diagrams that convey …

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 …