Chameleon: Foundation models for fairness-aware multi-modal data augmentation to enhance coverage of minorities
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 …
multi-modal settings, is a well-recognized concern. While there has been extensive effort in …
Coverage-based Data-centric Approaches for Responsible and Trustworthy AI.
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 …
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 …
information required for identifying minorities. On the other hand, it is known that machine …
[PDF][PDF] Data-centric reliability evaluation of individual predictions
N Shahbazi, A Asudeh - CoRR, abs/2204.07682, 2022 - academia.edu
At the same time that AI and machine learning are becoming central to human life, their
potential harms become more vivid. In the presence of such drawbacks, a critical question …
potential harms become more vivid. In the presence of such drawbacks, a critical question …