An applied reflection paper will be due at the end of each unit

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An applied reflection paper will be due at the end of each unit. In these short essays (1,200–1,500 words), your task will be to apply key concepts from that unit’s texts and lectures to a subject of your choosing. There will be a review and question-and-answer session on the final class meeting of each unit, and your papers will be due the following Sunday. After you submit the paper, you will receive feedback from your peers (and give feedback to others); you will then have the opportunity to revise the paper before it is graded by the instructor and/or TA. The feedback mechanism encourages students to support each other.

For ARP1, please select a topic and use concepts from at least two academic texts that we have read in this unit to analyze that topic. Topics may range from algorithmic systems (social media platforms, risk assessment tools, facial recognition, financial algorithms, etc.) to non-algorithmic ways of sorting and evaluating people and things (Yelp!, IQ tests, SAT tests, debates around identity-sorting mechanisms such as race and gender, and so on). The academic texts from this unit include the following:

• O’Neill (2016). “Introduction” and “Bomb parts: What is a model?” pp. 1–32 in Weapons of Math Destruction. New York: Broadway books.
• Tarleton Gillespie (2016). "Algorithm," in Ben Peters (ed.) Digital Keywords. Princeton: Princeton University Press.
• Massimo Mazzotti (2017). “Algorithmic life,” Los Angeles Review of Books, January 22.
• Langdon Winner (1980). “Do artifacts have politics?” Daedalus, 109 (1): 121–136.
• Ian Hacking (2006). “Making up people,” London Review of Books, August 17.
• Nick Seaver (2012). "Algorithmic recommendations and synaptic functions," Limn.
• Marion Fourcade (2016), "Ordinalization," Sociological Theory, 34(3): 175–195.
• John Cheney-Lippold (2017). “Categorization: Making data useful,” pp. 37–93 in We Are Data: Algorithms and the Makings of Our Digital Selves. New York: NYU Press.
• Kate Crawford and Trevor Paglen (2019), “Excavating AI: The Politics of Training Sets for Machine Learning,” AI Now Research Institute, September 19.
• Os Keyes (2019). “Counting the countless,” Real Life, April 8.