Deeplinks
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.
Podcast Episode: An AI Hammer in Search of a Nail