January 25, 2023
One of biggest challenges in the use of AI for healthcare is the issue of bias: instances when the application of an AI algorithm compounds existing inequities in socioeconomic status, race, ethnic background, religion, gender, disability, sexual orientation, or other criteria in ways that could amplify discrimination or adversely impact inequities in health systems. One of the many ways that this phenomenon of bias from AI health systems could occur is when an AI algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning process. In this session, I plan to speak on bias in health AI systems. I hope to discuss how AI algorithms can become biased, how they enter systems, and the harmful effects of algorithmic bias in AI for health care. Taking a critical human rights approach, I will also explore the legal and regulatory responses to bias in AI for health purposes.
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