Multi-disciplinary Fairness Considerations in Machine Learning for Clinical Trials

Multi-disciplinary Fairness Considerations in Machine Learning for Clinical Trials

Explore multi-disciplinary fairness considerations in machine learning for clinical trials in this 15-minute conference talk presented by Isabel Chien, Nina Deliu, Richard Turner, Adrian Weller, Sofia Villar, and Niki Kilbertus at the Association for Computing Machinery (ACM). Gain insights into the intersection of machine learning, clinical trials, and ethical considerations as the speakers delve into the complexities of ensuring fairness in AI-driven medical research. Learn about the challenges and potential solutions for implementing equitable machine learning algorithms in the context of clinical trials, and understand the importance of a multi-disciplinary approach to address these critical issues in healthcare and technology.