In this episode, how the future of artificial intelligence and machine learning education might look a lot more human.



When computer science was in its infancy, programmers quickly realized that though computers are astonishingly powerful tools, the results they achieve are only as good as the data you feed into them. (This principle was quickly formalized as GIGO: “Garbage In, Garbage Out.”) What was true in the era of the UNIVAC has proved still to be true in the era of machine learning: among other well-publicized AI fiascos, chatbots that have interacted with bigots have learned to spew racist invective, while facial-recognition software trained solely on images of white people sometimes fails to recognize people of color as human.

In this episode, we meet Prof. Catherine D’Ignazio of MIT’s Department of Urban Studies and Planning (DUSP) and Prof. Jacob Andreas and Harini Suresh of the Department of Electrical Engineering and Computer Science. In 2021, D’Ignazio, Andreas, and Suresh collaborated as part of the Social and Ethical Responsibilities of Computing initiative from the Schwarzman College of Computing in a project to teach computer science students in 6.864 Natural Language Processing to recognize how deep learning systems can replicate and magnify the biases inherent in the data sets that are used to train them.

Relevant Resources:

MIT OpenCourseWare

The OCW Educator Portal

Share your teaching insights

Case Studies in Social and Ethical Responsibilities of Computing

SERC website

Professor D’Ignazio’s faculty page

Professor Andreas’s faculty page

Harini Suresh’s personal website

Desmond Patton’s paper on analysis of communications on Twitter

Music in this episode by Blue Dot Sessions

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Sarah Hansen, host and producer

Brett Paci, producer

Dave Lishansky, producer

Script writing assistance by Aubrey Calaway

Show notes by Peter Chipman