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Workshop Programme:
Artificial intelligence (AI) has the potential to transform healthcare worldwide. bearing promises of increased accuracy, efficiency, and cost-effectiveness, in areas as diverse as drug discovery, clinical diagnosis, and disease management.
Furthermore, AI has been promoted as a tool that could expand the reach of quality healthcare to traditionally underserved patients and regions. But even with appropriate representation of marginalized communities with high-quality data, the social patterning of the data generation process can still produce AI that is bound to preserve and even scale existing disparities in care with resulting inequities in patient outcomes.
Creating algorithms from the digital exhaust of flawed human systems by AI developers who are not cognizant of the backstory of the data, risks cementing inequities as permanent fixtures in healthcare delivery systems. This course will introduce students to a portfolio of methodologies that learn patterns from the data. More importantly, it will explore data issues that if not addressed will have profound consequences on downstream prediction, classification, and optimization tasks.
Application: https://forms.gle/2vVKVGuLgzVm2Yd26