Fair Enough? Building Trustworthy and Equitable AI in Healthcare

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AI is transforming healthcare, but hidden biases in data and algorithms can undermine trust and lead to unequal outcomes. This session explores fairness in medical AI, using real-world case studies and the FAIR-MED/XFAIR-MED frameworks to show how bias can be detected, explained, and addressed. Attendees will gain practical insights into building AI systems that are not perfect, but “fair enough” to support safe and equitable decision-making.

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Categories: HEALTH, DATA SCIENCE / AI

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