DiscoverThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)Understanding the COVID-19 Data Quality Problem with Sherri Rose - #374
Understanding the COVID-19 Data Quality Problem with Sherri Rose - #374

Understanding the COVID-19 Data Quality Problem with Sherri Rose - #374

Update: 2020-05-11
Share

Description

Today we’re joined by Sherri Rose, Associate Professor at Harvard Medical School. 

Sherri’s research centers around developing and integrating statistical machine learning approaches to improve human health. We cover a lot of ground in our conversation, including the intersection of her research with the current COVID-19 pandemic, the importance of quality in datasets and rigor when publishing papers, and the pitfalls of using causal inference.

We also touch on Sherri’s work in algorithmic fairness, including the necessary emphasis being put on studying issues of fairness, the shift she’s seen in fairness conferences covering these issues in relation to healthcare research, and her paper “Fair Regression for Health Care Spending.”

Check out the complete show notes for this episode at twimlai.com/talk/374.

Comments 
In Channel
loading
Download from Google Play
Download from App Store
00:00
00:00
x

0.5x

0.8x

1.0x

1.25x

1.5x

2.0x

3.0x

Sleep Timer

Off

End of Episode

5 Minutes

10 Minutes

15 Minutes

30 Minutes

45 Minutes

60 Minutes

120 Minutes

Understanding the COVID-19 Data Quality Problem with Sherri Rose - #374

Understanding the COVID-19 Data Quality Problem with Sherri Rose - #374

Sam Charrington