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Addressing Algorithmic Harms: Practices and Provocations for Health AI

Unique threats to patient safety and well-being from artificial intelligence (AI) in health care applications, such as bias, are now well-recognized. At the same time, policy makers and regulators are grappling with the challenges of evaluating and regulating AI-based technologies in order to minimize these threats. Some potential harms arise from characteristics inherent in the technology, such as the “black box” nature of some machine learning, or the limitations of using AI to infer causation. Others arise from messy, incomplete, or unrepresentative datasets. Some of these challenges can be addressed technologically, while for others these ‘fixes’ may exacerbate the biases they intend to prevent. Others, still, will be reliant on systemic changes. This panel will discuss innovative regulatory, policy, and other approaches to minimizing harms to patients from health care AI.

Panelist: I. Glenn Cohen, JD (Harvard University)
Panelist: Melissa McCradden, PhD (University of Toronto)
Moderator: Kayte Spector-Bagdady, JD (University of Michigan)


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To continue the conversation please join fellow audience members for an informal discussion in a Zoom meeting immediately following the forum. The link will be placed in the Zoom Chat during the forum. For those who cannot attend the live event, the forum will be recorded and archived on the ELSIhub Video page.

Closed captioning and/or transcripts will be provided for live and recorded events.

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