Understanding the COVID-19 Data Quality Problem with Sherri Rose
Published:May 11, 2020 18:26
•1 min read
•Practical AI
Analysis
This article from Practical AI discusses a conversation with Sherri Rose, an Associate Professor at Harvard Medical School. The focus is on data quality, particularly in the context of the COVID-19 pandemic. The discussion covers the importance of rigorous data analysis and publication practices, the challenges of causal inference, and Sherri's work on algorithmic fairness within healthcare research. The article highlights the need for careful consideration of data quality and ethical implications in AI and healthcare.
Key Takeaways
- •Data quality is crucial, especially during a pandemic.
- •Rigorous research methods and publication practices are essential.
- •Algorithmic fairness is an important consideration in healthcare research.
Reference
“The article doesn't provide a direct quote, but it focuses on the conversation with Sherri Rose about data quality and related topics.”