Lecture 12 covered Data Science and data engineer as a Trading Zone (cf., https://github.com/data-ppf/data-ppf.github.io/wiki/Lecture-12 )

Lecture 13 transitions to ethics, a point many of you raised all term but in particular on Monday.

In an unusual assignment, instead of assigning many pieces, we're just assigning one: sections 6.1-6.6 of a book which came out this year with an excellent chapter on ethics. (cf., https://data-ppf.slack.com/files/U3SJU2P6W/FA3GN580Y/salganik-ch6_7.pdf )

Since it's a textbook it has lots of great references; the optional assignment is please to track down references of interest to you. It's a real pleasure to read. Enjoy!!

Some of Prof. Wiggins's own thinking on the subject is here:

http://datascience.columbia.edu/ethical-principles-okrs-and-kpis-what-youtube-and-facebook-could-learn-tukey

For Tuesday, we're moving from

• a chronology of how we make sense of our world through data,
• and how that came to be our set of assumptions and methods, to
• consequences of those assumptions and methods.

Specifically we'll read

1. pp260-308 (sec 6.1-6.8) of Ch 6 of Matt Salganik's forthcoming book "Bit by Bit" (https://data-ppf.slack.com/files/chris/F4ZJ5RS5D/bit-by-bit-open-review-ch6-73p.pdf ; website= http://www.bitbybitbook.com/ ). Matt got his PhD in sociology at Columbia in 2007.

2. a very recent --- last month!! -- piece on ethics and big data http://journals.plos.org/ploscompbiol/article/comments?id=10.1371/journal.pcbi.1005399 "Ten simple rules for responsible big data research"

authors include

• danah boyd and Kate Crawford, whose essay started the class;
• Arvind Narayanan, coauthor of the Netflix paper Jillian posted; and
• Columbia's own Alondra Nelson