It often feels like machine learning experts are running around with a hammer, looking at everything as a potential nail - they have a system that does cool things and is fun to work on, and they go in search of things to use it for. But what if we flip that around and start by working with people in various fields - education, health, or economics, for example - to clearly define societal problems, and then design algorithms providing useful steps to solve them?

Rediet Abebe, a researcher and professor of computer science at UC Berkeley, spends a lot of time thinking about how machine learning functions in the real world, and working to make the results of machine learning processes more actionable and more equitable.

Abebe joins EFF's Cindy Cohn and Danny O’Brien to discuss how we redefine the machine learning pipeline - from creating a more diverse pool of computer scientists to rethinking how we apply this tech for the betterment of society’s most marginalized and vulnerable - to make real, positive change in people’s lives.

In this episode you’ll learn about:

• The historical problems with the official U.S. poverty measurement
• How machine learning can (and can’t) lead to more just verdicts in our criminal courts
• How equitable data sharing practices could help nations and cultures around the world
• Reconsidering machine learning’s variables to maximize for goals other than commercial profit. 
   

If you have any feedback on this episode, please email [email protected]. Please visit the site page at https://eff.org/pod208 where you’ll find resources – including links to important legal cases and research discussed in the podcast and a full transcript of the audio. 

This podcast is supported by the Alfred P. Sloan Foundation's Program in Public Understanding of Science and Technology.

Music for How to Fix the Internet was created for us by Reed Mathis and Nat Keefe of BeatMower. 

This podcast is licensed Creative Commons Attribution 4.0 International, and includes the following music licensed Creative Commons Attribution 3.0 Unported by their creators: 

http://dig.ccmixter.org/files/djlang59/59729

Probably Shouldn't by J.Lang

http://dig.ccmixter.org/files/Skill_Borrower/41751

Klaus by Skill_Borrower 

http://dig.ccmixter.org/files/airtone/58703

commonGround by airtone

http://dig.ccmixter.org/files/JeffSpeed68/56377

Smokey Eyes by Stefan Kartenberg 

http://dig.ccmixter.org/files/NiGiD/62475

Chrome Cactus by Martijn de Boer (NiGiD)