Links to readings:

1. "The Automation Charade" by Astra Taylor: logicmag.io/failure/the-automation-charade/

2. "The Rise of Pseudo-AI" by Olivia Solon: https://www.theguardian.com/technology/2018/jul/06/artificial-intelligence-ai-humans-bots-tech-companies

3. "Justice for Data Janitors" by Lilly Irani www.publicbooks.org/justice-for-data-janitors/

Automation; the Future of Work; Fake A.I.

0:00-6:22: Introduction for my students

6:23-19:49: Lecture on Bernard Marr, What is Big Data: https://www.bernardmarr.com/default.asp?contentID=766

19:50-end: Lecture on Lisa Gitelman and Virginia Jackson, Introduction to Raw Data is an Oxymoron: https://raley.english.ucsb.edu/wp-content/Engl800/RawData-excerpts.pdf

Also mentioned: Timandra Harkness, "What is Knowledge in the Age of Big Data?:" https://www.youtube.com/watch?v=tdilkRC4YVw&t=189s

Raw Data is an Oxymoron

0:00-6:24  Introduction for my students
6:25-23:26: Lecture on Chris Anderson, "The End of Theory: The Data Deluge Makes the Scientific Method Obsolete:" https://www.wired.com/2008/06/pb-theory/
23:26-42:29: Lecture on Nathan Jurgenson, "View From Nowhere: on the Cultural Ideology of Big Data:" https://thenewinquiry.com/view-from-nowhere/
42:30-conclusion: Lecture on Rob Kitchin, "Big Data, New Epistemologies and Paradigm Shifts:" https://journals.sagepub.com/doi/pdf/10.1177/2053951714528481

Big Data and the End of Theory

0:00- 5:59: Introduction for my students

6:00-16:24: Lecture on John Cheney-Lippold, “Who We Are as Data May Soon Become More Important than Who We Are as People:" 
 https://docs.google.com/document/d/1T8iYx-rmu3Urtxh1EcesJq48zwz-NSE-HRPya5tY2cs/edit?usp=sharing

16:25-22:16: Lecture on "Interview with John Cheney-Lippold," 
 https://www.fromthesquare.org/we-are-data/#.XhucGVNKhQI

22:17-conclusion: Lecture on Hong, "Data's Intimacy: Machinic Sensibility and the Quantified Self," https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1050&context=cpo

The Algorithmic You

These podcasts are for students in my online course "Introduction to Data in Social Context."  Track descriptions include links to assigned readings.

Every episode begins with an introduction specific to my students. I've left those in because they're hard to edit out seamlessly, but you may want to skip them.

For more information about my teaching and research, please see www.o-culus.com.