Dr. Eric Davis walks us through what it means for a data model to be trustworthy, what common pitfalls predictive models run into, reproducibility issues, and what can be done. We chat about how subject area experts are expected to be many things: statisticians, computer scientists, and mathematicians, and how that can sometimes lead to mistakes. We also look at the COVID-19 pandemic and how data models affect decision-making.

https://www.imagwiki.nibib.nih.gov/ https://www.imagwiki.nibib.nih.gov/content/committee-credible-practice-modeling-simulation-healthcare-description https://www.biorxiv.org/content/10.1101/2020.08.07.239855v1 https://www.imagwiki.nibib.nih.gov/content/10-simple-rules-conformance-rubric

You can watch this episode on our Youtube Channel:  https://youtube.com/c/BuildingBetterSystemsPodcast

Joey Dodds: https://galois.com/team/joey-dodds/ 

Shpat Morina: https://galois.com/team/shpat-morina/  

Eric Davis: https://galois.com/team/eric-davis/ 

Galois, Inc.: https://galois.com/ 

Contact us: podcast@galois.com

 

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Building Better Systems

#8: Eric Davis – Building Better Data Models

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