Title
Fairness in Machine Learning for Healthcare
Publication Date
8-23-2020
Document Type
Conference Proceeding
Abstract
The issue of bias and fairness in healthcare has been around for centuries. With the integration of AI in healthcare the potential to discriminate and perpetuate unfair and biased practices in healthcare increases many folds The tutorial focuses on the challenges, requirements and opportunities in the area of fairness in healthcare AI and the various nuances associated with it. The problem healthcare as a multi-faceted systems level problem that necessitates careful of different notions of fairness in healthcare to corresponding concepts in machine learning is elucidated via different real world examples.
Publication Title
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
First Page
3529
Last Page
3530
DOI
10.1145/3394486.3406461
Open Access Status
Licensed
Recommended Citation
Ahmad, M. A., Patel, A., Eckert, C., Kumar, V., & Teredesai, A. (2020). Fairness in Machine Learning for Healthcare. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 3529–3530. https://doi.org/10.1145/3394486.3406461
