Algorithmic Culture

( #AlgoCult )


SI 710.001 / COMM 820.002 -- Fall 2018
Prof. Sandvig, University of Michigan

"[There is a] need to create a new field around the social algorithm, which examines the interplay of social and computational code."
    –David Lazer (2015)

"'Algorithm studies' is the critical study of the social, political and cultural life of the algorithm and its conditions of change, evolution and possibility."
    –Jenna Ng and David Theo Goldberg (2015)

"That we are now turning to algorithms to identify what we need to know is as momentous as having relied on credentialed experts, the scientific method, common sense, or the word of God."
    –Tarleton Gillespie (2015)

"An algorithm must be seen to be believed, and the best way to learn what an algorithm is all about is to try it."
    –Donald Knuth (1968)




About the Class


Prof. Christian Sandvig
Office: 5385 North Quad
My mailbox is in the 3rd floor School of Information mailbox area, in the 3rd floor kitchen (3351 North Quad)
Office Hours: 3:00-4:00 p.m. Mondays and by appointment

Course Description

The humanistic and social scientific study of information and communication technologies is fundamentally concerned with one problem: these socio-technical systems shape what we experience. This concern is longstanding. Worries about technology, automation and computerization have a long history. "Algorithm" is a very old word. Yet recently there has been an explosion of new scholarly work examining algorithms and culture, a pairing of two topics that many people find at least 50% mysterious. Researchers claim that the contemporary use of automated ("algorithmic") systems to produce, consume, curate, rank, filter, mediate, store, and sell social and cultural life heralds a set of pivotal transformations for culture itself.

To investigate these latest claims, this course surveys contemporary research that considers the implications of computational processes that treat culture as data. We will draw from science and technology studies, information science, anthropology, communication, media studies, legal theory, sociology, and computer science, with additional contributions from psychology and philosophy. Although we will consider "natively algorithmic" digital cultural products (such as digital experiences like social media platforms and video games), our overall goal will be to examine the potential transformation of any form of culture. Due to our multidisciplinary approach, no particular technical, humanistic, or social scientific background is required.

Learning Objectives

Course Credit

Class Requirements

Students will be responsible for a seminar paper proposal due midway through the term and a (research) seminar paper of about 25 double-spaced pages due at the end of term. A seminar paper should be similar in scope and format to a scholarly conference paper. In addition, there will be short assignments ("weekly questions") due one hour before the beginning of each class meeting when reading is assigned. These will be read and discussed in class but not graded. All assignments will be turned in electronically.

No late work! No incompletes! (Without cause.)

Required Books

There are no required books. Readings will be distributed electronically. These are either free on the Web or use password-protected links to PDFs stored in Canvas.

Recommended Books

These books are recommended in the sense that every doctoral student working in the social sciences and humanities should own them already. If you don't own them, you should buy them! They are highly recommended.

  1. Howard S. Becker & Pamela Richards. (2007). Writing for Social Scientists: How to Start and Finish Your Thesis, Book, or Article. Chicago: University of Chicago Press. (any edition is fine.) [Note that although the phrase "social scientist" is in the title of the book, this book is equally relevant to researchers from the humanities and/or computing who don't identify with the "social science" phrase.]
  2. William Strunk, Jr. & E. B. White. (2000). The Elements of Style. New York: Longman. (Any edition is fine except for the 1920 or 2011 "Original Edition" that does not include E. B. White. Be sure it has E. B. White. If it has Kalman as a co-author too, I think that is OK -- this just means it is the illustrated edition.)


These dates and readings will be adjusted to reflect our progress (or lack of it). This means that you should check the class Web site regularly for updates.


Week 1 -- 10 Sep (Mon): Introduction

Week 2 -- 17 Sep (Mon): What's the Problem? Why Study Algorithms and Culture?

  • Please post your weekly question one hour before class begins.
  • O'Neil, Cathy. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown. Excerpts from Ch. 1: Bomb Parts: What is a Model?. (On Canvas)
  • Pasquale, Frank. (2015). The Black Box Society: The Secret Algorithms That Control Money and Information. Cambridge: Harvard University Press. Excerpts from Ch. 1: The Need to Know and Ch. 6: Toward an Intelligible Society.) (On Canvas)
  • Vaidhyanathan, Siva. (2018). Antisocial Media: How Facebook Disconnects Us and Undermines Democracy. Cambridge: Oxford University Press. All of Ch. 1: The Pleasure Machine and Ch. 3: The Attention Machine (On Canvas)
  • Ziewitz, Malte. (2015). "Governing Algorithms: Myth, Mess, and Methods." Science, Technology & Human Values. (On Canvas)
  • Gillespie, Tarleton. (2014). "The Relevance of Algorithms." In Media Technologies: Essays on Communication, Materiality, and Society, edited by Tarleton Gillespie, Pablo Boczkowski, and Kirsten Foot, 167-194. Cambridge, MA: MIT Press. (On Canvas)
  • OPTIONAL: Seaver, Nick. (2017, July-December). Algorithms as Culture. Big Data and Society: 1-12. (On Canvas)
  • ALSO DISCUSSED: Ed Finn. (2017). What Algorithms Want: Imagination in the Age of Computing. Cambridge, MA: MIT Press.

Week 3 -- 24 Sep (Mon): What are algorithms? What are cultural algorithms?

  • Please post your weekly question one hour before class begins.
  • Wangsness, T. and J. Franklin. 1966. "Algorithm" and "formula." Communications of the ACM 9(4), 243. (and 2 replies) (On Canvas)
  • Striphas, Ted. 2015. "Algorithmic Culture." European Journal of Cultural Studies 18(4-5): 395-412. (On Canvas)
  • Gillespie, Tarleton. (2016). "Algorithm." In Digital Keywords, edited by Ben Peters. Princeton, N.J.: Princeton University Press. (On Canvas)
  • Mahnke, Martina and Emma Uprichard. 2014 "Algorithming the Algorithm." In Society of the Query Reader: Reflections on Web Search. René König and Miriam Rasch, eds. Amsterdam: Institute of Network Cultures. Online:
  • Kitchin, Rob. 2017. "Thinking Critically about and Researching Algorithms." Information, Communication, and Society 20(1).(On Canvas)
  • OPTIONAL: Blass, A. & Gurevich, Y. (2003). Algorithms: A Quest for Absolute Definitions. Bulletin of the European Association for Theoretical Computer Science 81. Online:
  • OPTIONAL: Dourish, Paul. (2016, July-December). Algorithms and Their Others. Big Data & Society: 1-11. (On Canvas)
  • OPTIONAL: Totaro, Paolo, and Ninno, Domenico. (2014). "The Concept of Algorithm as an Interpretative Key of Modern Rationality." Theory, Culture & Society 31 (4): 29-49. (On Canvas)
  • ALSO DISCUSSED: Lucas D. Introna & Helen Nissenbaum. (2000). Shaping the Web: Why the Politics of Search Engines Matters. The Information Society 16(3): 169-185.

Week 4 -- 1 Oct (Mon): How should we conceptualize "culture" itself?

  • Please post your weekly question one hour before class begins.
  • Gans, Herbert J. (1999). Popular Culture and High Culture. 2nd ed. Basic Books: New York. (excerpts from Introduction: The Popular Culture-High Culture Distinction.) (On Canvas)
  • Williams, Raymond. (1958). Culture is Ordinary. London: Verso. (Editor's introduction and short excerpt.) (On Canvas)
  • Striphas, Ted. (2014). Culture. In: Digital Keywords. Ben Peters (ed.) Excerpt. (On Canvas)
  • Castells, Manuel. (2000). Materials for an exploratory theory of the network society. British Journal of Sociology 51(1): 5-24. (On Canvas)
  • Striphas, Ted. (2014). "Culture now has two audiences: People and Machines." (Interview.) Online:
  • OPTIONAL: Adorno, T., & Horkheimer, M. Dialectic of Enlightenment. Edmund Jephcott, trans. Stanford University Press (1947/2002). Read: The Culture Industry: Enlightenment as Mass Deception. (On Canvas)
  • OPTIONAL: Benjamin, Walter. 1936/1961. The Work of Art in the Age of Mechanical Reproduction. Harry Zohn, trans. (On Canvas)

Week 5 -- 8 Oct (Mon): How do "users" (or "algorithmic subjects") experience and think about algorithms and culture?

Week 6 -- 15 Oct (Mon): NO CLASS (Fall Study Break)

Week 7 -- 22 Oct (Mon): How do "technologists" or "developers" experience and think about algorithms and culture?

Week 8 -- 29 Oct (Mon): Seminar Paper Proposal Workshop Day

  • Seminar paper proposal due.
  • As discussed in class, this week your task is to use the library to generate ideas and to identify relevant outside research for your potential paper topic. Then write a seminar paper proposal.

Week 9 -- 5 Nov (Mon): Prediction

  • SPECIAL EVENT: Joint Meeting with COMM 820: "Media, Time, and Digital Life"
  • ROOM CHANGE! We will meet in 3100 North Quad (The Ehrlicher Room). Instructions: Follow path #2 on these directions to find the room. It is important to use the correct stairs or elevator or you will not be able to enter the room. There is only one correct stairwell/elevator and it is probably not one you've ever used (unless you've been to this room before).
  • John Urry. (2016). What is the future? London: Polity. Read Introduction, Part I, Part II, and Conclusion (a.k.a. chapters 1-6 and 10). (On Canvas)
  • Andrew Guthrie Ferguson. (2017). The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement. New York: New York University Press. Read Ch. 1-3, 5-6, and conclusion. (On Canvas)
  • V. S. Subrahmanian and Srijan Kumar. (2017). Predicting human behavior: The next frontiers (ESSAY) Science 355 (6324): 489. (On Canvas)
  • Mackenzie, Adrian. (2015). "The Production of Prediction: What Does Machine Learning Want?" European Journal of Cultural Studies 18(4/5): 429–45. (On Canvas)
  • OPTIONAL: Strogatz, Steven; Lo, Andrew W.; Fowler, James; & Lloyd, Seth. (2014). "What can't we predict with math?" World Science Festival. (video excerpt, ~5 minutes)
  • OPTIONAL: Zuiderveen Borgesius, F.J. (2014). Ch. 2: "Behavioural Targeting" in: Improving Privacy Protection in the Area of Behavioural Targeting
  • OPTIONAL: James Carey and John J. Quirk. (1992). "The History of the Future" pp. 173-200 in Communication as Culture: Essays on Media and Society (rev. ed.). New York: Routledge. (On Canvas)
  • OPTIONAL: Collins, Harry & Pinch, Trevor. (1998). Tidings of Comfort and Joy: The Seven Wise Men and the Science of Economics (book chapter) Ch. 5 of "The Golem at Large" Cambridge University Press.

Week 10 -- 12 Nov (Mon): What new methods are being developed to investigate algorithmic systems? What is algorithm auditing?

Week 11 -- 19 Nov (Mon): How can algorithmic processes provide accountability? How are algorithms governed?

Week 12 -- 26 Nov (Mon): Is algorithmic judgement inhuman? What would that mean?

Week 13 -- 3 Dec (Mon): How do we decide what constitutes an ethical, fair, or just algorithm?

  • Please post your weekly question one hour before class begins.
  • Mittelstadt, Brent Daniel; Allo, Patrick; Taddeo, Mariarosaria; Wachter, Sandra; and Floridi, Luciano. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society. (On Canvas)
  • Ananny, Mike. (2016). Toward an Ethics of Algorithms. Science, Technology, & Human Values 41(1): 93-117. (On Canvas)
  • Selbst, Andrew D.; boyd, danah, Friedler, Sorelle A.; Venkatasubramanian, Suresh; & Vertesi, Janet. (forthcoming). Fairness and Abstraction in Sociotechnical Systems. Proc. ACM FAT*.
  • Ananny, M. and Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society 20.3 (2018): 973-989. (On Canvas)
  • Kate Crawford and Vladan Joler. (2018). Anatomy of an AI System. Online: (one BIG page -- please zoom in and look carefully)
  • OPTIONAL: Floridi, Luciano & Sanders, J. W. (2004). On the morality of artificial agents. Minds and Machines 14(3):349-379.

Week 14 -- 10 Dec (Mon): What challenges and opportunities does machine learning pose in a cultural context?

  • SPECIAL EVENT: Joint Meeting with SI 670: "Applied Machine Learning" for the first half of our scheduled meeting time.
  • ROOM CHANGE! We will meet in 1255 North Quad (right next to our usual room).
  • Please post your weekly question one hour before class begins.
  • Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78. Online:
  • Wallach, Hannah. (2014). Big Data, Machine Learning, and the Social Sciences. NIPS 2014 workshop keynote. Online:
  • Burrell, Jenna. (2015). "How the Machine ‘Thinks:’ Understanding Opacity in Machine Learning Algorithms." Big Data & Society. (On Canvas)
  • Hallinan, Blake, and Ted Striphas. 2014. "Recommended for You: The Netflix Prize and the Production of Algorithmic Culture." New Media & Society. (On Canvas)
  • OPTIONAL: Mackenzie, Adrian. (2015). Machine Learning and Genomic Dimensionality: From Features to Landscapes. In: Postgenomics: perspectives on biology after the genome. ed. / Sarah Richardson; Hallam Stevens. Durham, N.C.: Duke University Press. pp. 73–102. (On Canvas)

17 Dec (Mon): Final paper due

Final paper due at 10:30 a.m.
This is the end of the scheduled final exam period for this course according to the registrar's office. Submitting the paper will count as the final examination for this seminar; there is no other final examination. Turn in your paper via the Canvas Assignments system.

Class Policies

Our Discussions

This seminar practices the "Guidelines for Dialogue" developed by students and faculty from the University of Michigan Program on Intergroup Relations. That means that we will do our best to:

  1. Maintain confidentiality. We want to create an atmosphere for open, honest exchange.
  2. Commit to learning from each other. We will listen to other and not talk at each other. We acknowledge differences among us in backgrounds, skills, interests, identities and values. We realize that it is these very differences that will increase our awareness and understanding through this process.
  3. Not demean, devalue, or "put down" people for their experiences, lack of experiences, or difference in interpretation of those experiences.
  4. Trust that people are always doing the best they can. We will give each other the benefit of the doubt. We will assume we are all trying our hardest and that our intentions are good even when the impact is not.
  5. Challenge the idea and not the person. If we wish to challenge something that has been said, we will challenge the idea or the practice referred to, not the individual sharing this idea or practice.
  6. Speak our discomfort. If something is bothering us, we will share this with the group. Often our emotional reactions to this process offer the most valuable learning opportunities.
  7. Step Up, Step Back. We will be mindful of taking up much more space than others. On the same note, empower ourselves to speak up when others are dominating the conversation.
  8. Not to freeze people in time. We are all works in progress. We will be willing to change and make space for others to do so. Therefore we will not assume that one comment or one opinion made at one time captures the whole of a person's character.

--The Program on Intergroup Relations, University of Michigan, 2012

Academic Integrity

Unless otherwise specified in an assignment all submitted work must be your own, original work. Any excerpts, statements, or phrases from the work of others must be clearly identified as a quotation, and a proper citation provided. Any violation of the School of Information's policy on Academic and Professional Integrity (stated in the Master’s and Doctoral Student Handbooks) will result in serious penalties, which might range from failing an assignment, to failing a course, to being expelled from the program. Violations of academic and professional integrity will be reported. Consequences impacting assignment or course grades are determined by the faculty instructor; additional sanctions may be imposed by the Assistant Dean for Academic and Student Affairs.

Accommodations for Students with Disabilities

If you think you need an accommodation for a disability, please let me know at your earliest convenience. Some aspects of this course, the assignments, the in-class activities, and the way we teach may be modified to facilitate your participation and progress. As soon as you make me aware of your needs, we can work with the Office of Services for Students with Disabilities (SSD) to help us determine appropriate accommodations. SSD (734-763-3000; typically recommends accommodations through a Verified Individualized Services and Accommodations (VISA) form. I will treat any information that you provide in as confidential a manner as possible.

Student Mental Health and Wellbeing

The University of Michigan is committed to advancing the mental health and wellbeing of its students. If you or someone you know is feeling overwhelmed, depressed, and/or in need of support, services are available. For help, contact Counseling and Psychological Services (CAPS) at (734) 764-8312 and during and after hours, on weekends and holidays, or through its counselors physically located in schools on both North and Central Campus. You may also consult University Health Service (UHS) at (734) 764-8320 and , or for alcohol or drug concerns, see For a listing of other mental health resources available on and off campus, visit: