Craig Martell says he won the career lottery. After studying logic, philosophy, political science, and political theory, he completed a Ph.D. in computer science and found his way to machine learning, a field he thoroughly enjoys. After spending time at Dropbox and LinkedIn, Craig headed to Lyft, where he runs the LyftML engineering team. He’s also an adjunct professor at Northeastern University in Seattle.

We kick off Season 2 of Me, Myself, and AI discussing a particular trend Craig has seen in the AI and machine learning space: As organizations depend more on technology-driven solutions to solve business problems, algorithms themselves are less important than how they fit into an overall engineering product pipeline and product development road map. Craig shares his thoughts about what this shift means for academic education and cross-functional collaboration in organizations, and the hosts pick his brain about how to combat unconscious bias. Read the episode transcript here.

Read more about our show and follow along with the series at https://sloanreview.mit.edu/aipodcast.

To find out more about the movie Coded Bias, which Craig mentions during the interview, visit https://www.codedbias.com. To learn more about the work of MIT Media Lab researcher Joy Buolamwini, visit her page on the lab's website: https://www.media.mit.edu/people/joyab/overview/

Me, Myself, and AI is a collaborative podcast from MIT Sloan Management Review and Boston Consulting Group and is hosted by Sam Ransbotham and Shervin Khodabandeh. Our engineer is David Lishansky, and the coordinating producers are Allison Ryder and Sophie Rüdinger.

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Guest bio: Craig Martell is head of machine learning at Lyft and an adjunct professor of machine learning for Northeastern University’s Align program in Seattle. Before joining Lyft, he was head of machine intelligence at Dropbox, led a number of AI teams and initiatives at LinkedIn, and was a tenured professor at the Naval Postgraduate School in Monterey, California. Martell has a Ph.D. in computer science from the University of Pennsylvania and is coauthor of Great Principles of Computing (MIT Press, 2015).

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