Data Nation
Data Dilemmas in Health
Sep 6, 2023
Regina Barzilay, computer scientist and expert in natural language processing, talks about data collection, privacy, bias, and 'distributional shift' in healthcare algorithms. She addresses the lack of diversity in data sets, challenges in integrating data in drug discovery, biases in AI and healthcare, and challenges in insurance and personalized medicine.
35:09
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Quick takeaways
- The lack of diverse and representative data sets in healthcare research leads to biased models that are not effective for underrepresented populations.
- Bias in healthcare data can result from lack of diversity, unintentional confounding variables, and missing data, highlighting the importance of identifying and mitigating bias in healthcare models.
Deep dives
The Importance of Diverse and Accessible Data in Clinical Trials
The podcast discusses the importance of diverse and accessible data in clinical trials. The speaker highlights the lack of data sets available for research in various areas, such as mammograms, which pose challenges for machine learning and data science research. Data sets that exist often lack diversity, with populations like African-Americans and Asians being underrepresented. This underrepresentation leads to biased models that are not effective for these populations. The speaker emphasizes the need for diverse and representative data sets in order to develop accurate and effective healthcare models.
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