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Generalization in Healthcare AI: Evaluation of a Clinical Large Language Model
Overall, this study provides new insights for enhancing the deployment of large language models in the societally important domain of healthcare, and improving their performance for broader populations.
Impact on Public Health Decision Making by Utilizing Big Data Without Domain Knowledge
Relevant to such uses, important examples of bias in the use of AI are evident when decision-making based on data fails to account for the robustness of the data, or predictions are based on spurious correlations.
Understanding Disparities in Post Hoc Machine Learning Explanation
Previous work has highlighted that existing post-hoc explanation methods exhibit disparities in explanation fidelity (across 'race' and 'gender' as sensitive attributes), and while a large body of work focuses on mitigating these issues at the explanation metric level, the role of the data generating process and black box model in relation to explanation disparities remains largely unexplored.
Out of Distribution Performance of State of Art Vision Model
In this study, we investigate the performance of 58 state-of-the-art computer vision models in a unified training setup based not only on attention and convolution mechanisms but also on neural networks based on a combination of convolution and attention mechanisms, sequence-based model, complementary search, and network-based method.
