Chameleon: Foundation models for fairness-aware multi-modal data augmentation to enhance coverage of minorities

M Erfanian, HV Jagadish, A Asudeh - arXiv preprint arXiv:2402.01071, 2024 - arxiv.org
The potential harms of the under-representation of minorities in training data, particularly in
multi-modal settings, is a well-recognized concern. While there has been extensive effort in …

Gender in Pixels: Pathways to Non-binary Representation in Computer Vision

E Beretta - Proceedings of the AAAI/ACM Conference on AI, Ethics …, 2024 - ojs.aaai.org
In the field of Computer Vision (CV), the study of bias, including gender bias, has received a
significant area of attention in recent years. However, these studies predominantly operate …

Coverage-based Data-centric Approaches for Responsible and Trustworthy AI.

N Shahbazi, M Erfanian, A Asudeh - IEEE Data Eng. Bull., 2024 - sites.computer.org
The grand goal of data-driven decision systems is to help make decisions easier, more
accurate, at a higher scale, and also just. However, data-driven algorithms are only as good …

Mining the Minoria: Unknown, Under-represented, and Under-performing Minority Groups

M Dehghankar, A Asudeh - arXiv preprint arXiv:2411.04761, 2024 - arxiv.org
Due to a variety of reasons, such as privacy, data in the wild often misses the grouping
information required for identifying minorities. On the other hand, it is known that machine …