On today’s show, we’re joined by Genevera Allen, associate professor of statistics in the EECS Department at Rice University, Founder and Director of the Rice Center for Transforming Data to Knowledge and Investigator with the Neurological Research Institute with the Baylor College of Medicine.
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Back in February, Genevera gave a talk at the American Association for the Advancement of Science meeting titled “Can We Trust Data-Driven Discoveries?,” that caused quite a stir amongst many members of the ML community. In our conversation, Genevera details the goals of her talk and gives us a few use cases outlining the shortcomings of current machine learning techniques. We also discuss reproducibility, including inference vs discovery, and the lack of terminology for many of the various reproducibility issues.
From the Interview
- Article: Rice statistician’s warning about machine learning grabs headlines around the globe
- Article: Why a data scientist warns against always trusting AI’s scientific discoveries
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“More On That Later” by Lee Rosevere licensed under CC By 4.0