LECTURE: Week (4) - Algorithms and automation

This lecture will be held Tuesday, February 16, 2:30pm-3:45pm central time -- live in-person in 3650 Humanities and simultaneously streamed online at go.wisc.edu/n6986j for remote viewing. The lecture recording and slides will be posted below within 24 hours.

This week's focus is on issues of algorithms and automation in a wide variety of careers, and how a liberal arts and sciences education prepares one for such a future.

Slides - INTER-LS 215 week (4) - Spring 2021.pdf Download Slides - INTER-LS 215 week (4) - Spring 2021.pdf

Supplemental material

Here is the separate Jeopardy! video with Watson victorious, in its entirety, for those who are interested in watching or downloading this:

My lecture references some academic work on issues of automation in professional careers. Here are two pieces that might be useful (and which you might even be able to incorporate as sources in your technology essays):

David Autor, "Why Are There Still So Many Jobs? The History and Future of Workplace Automation Download Why Are There Still So Many Jobs? The History and Future of Workplace Automation
," Journal of Economic Perspectives 29:3 (Summer 2015). An MIT economist, Autor argues, "the interplay between machine and human comparative advantage allows computers to substitute for workers in performing routine, codifiable tasks while amplifying the comparative advantage of workers in supplying problem-solving skills, adaptability, and creativity. [...] Focusing only on what is lost misses a central economic mechanism by which automation affects the demand for labor: raising the value of the tasks that workers uniquely supply."
Zeynep Tufekci, "Algorithmic Harms beyond Facebook and Google: Emergent Challenges of Computational Agency Download Algorithmic Harms beyond Facebook and Google: Emergent Challenges of Computational Agency
," 13 Colorado Technology Law Journal 203 (2015). This is a less optimistic view of the way algorithmic workplaces will affect professional labor -- through exerting surveillance and control rather than enhancing "human comparative advantage". Dr. Tufekci is an Associate Professor at the UNC School of Information and Library Science, and I think it is interesting to contrast her arguments with those of Autor.

Are these visions in conflict, or might both have implications for the career community you are exploring?

And finally, here is a bonus video which I think you will find interesting -- a twelve-minute TED talk about the biases inherent in many AI and big data systems. This video by filmmaker Robin Hauser combines aspects of both Autor's and Tufekci's arguments. This theme will link back to the second part of our course when we focus on diversity and inclusion more closely. I would encourage you to watch this and provide feedback in the General INTER-LS 215 course questions and lecture comments discussion