Computer vision models have known performance disparities across attributes such as
gender and skin tone. This means during tasks such as classification and detection, model …

Transfer learning is beneficial by allowing the expressive features of models pretrained on
large-scale datasets to be finetuned for the target task of smaller, more domain-specific …

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 …

Image captioning models are known to perpetuate and amplify harmful societal bias in the
training set. In this work, we aim to mitigate such gender bias in image captioning models …

Text-to-image (T2I) models have emerged as a significant advancement in generative AI;
however, there exist safety concerns regarding their potential to produce harmful image …

Human-centric computer vision (HCCV) data curation practices often neglect privacy and
bias concerns, leading to dataset retractions and unfair models. HCCV datasets constructed …

Recently, transformers have become incredibly popular in computer vision and vision-
language tasks. This notable rise in their usage can be primarily attributed to the capabilities …

Debates in AI ethics often hinge on comparisons between AI and humans: which is more
beneficial, which is more harmful, which is more biased, the human or the machine? These …

Deep neural networks are prone to capture correlations between spurious attributes and
class labels leading to low accuracy on some combinations of class labels and spurious …

This paper examines NSFW (Not Safe For Work) image classifiers for content filtering.
Through an audit of three prevalent NSFW classifiers, we analyze the relationship between …