Algorithmic Fairness in Healthcare
Sherri highlights the critical need for multiple metrics in evaluating algorithms, particularly in healthcare, where marginalized groups are often overlooked. She emphasizes that many clinical papers fail to consider the potential harms of deploying algorithms without assessing their impact on these vulnerable populations. The current state of fairness in clinical practice is lacking, and Sherri advocates for a shift towards more comprehensive evaluations that include group fit metrics.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Understanding the COVID-19 Data Quality Problem with Sherri Rose - #374
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