Workshop: Health AI Systems Thinking for Equity

Underlying bias in the data used for AI modelling may have profound downstream effects on models. Hosted by an international faculty, this course will explore the effects of the data generation process on AI models, including mitigation strategies.   

Learning Objectives 

• Work with data scientists, social scientists and clinicians across the life cycle of AI health applications.
• Develop a critical understanding of how the dataset came about from collection to aggregation to standardization.
• Think about potential downstream harm from algorithm implementation.

Leo Anthony Celi MD Phd, Harvard T.H. Chan School of Public Health.

Martin Sillesen MD PhD, Clinical Research Associate Professor of Surgery.

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Organizer: IDA Risk - IDA, Engineering Society

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