BRIO Tool
BRIO is a suite of tools designed to analyze and mitigate bias and unfairness in AI systems, particularly focusing on model-agnostic bias detection and fairness risk evaluation. Current research emphasizes applying BRIO to assess fairness in specific domains like credit scoring, and explores various model architectures (e.g., neural networks, transformers) and algorithms (e.g., random features, Bayesian regularization) for improved accuracy and efficiency. The significance of BRIO lies in its potential to promote ethical AI development and deployment by providing quantitative methods for identifying and addressing societal biases embedded within AI models, thereby enhancing fairness and trustworthiness in various applications.
Papers
Investigating Sindy As a Tool For Causal Discovery In Time Series Signals
Andrew O'Brien, Rosina Weber, Edward Kim
TAToo: Vision-based Joint Tracking of Anatomy and Tool for Skull-base Surgery
Zhaoshuo Li, Hongchao Shu, Ruixing Liang, Anna Goodridge, Manish Sahu, Francis X. Creighton, Russell H. Taylor, Mathias Unberath