Learning goals

  • Student can separate the roles of humans, code and data in algorithmic systems and recognise how they form assemblages.
  • Student can contrast the benefits and challenges algorithmic systems have across at least two application domains in the society.
  • Students can contrast different methodological choices to study algorithmic systems.
  • Student can compose a research protocol to evaluate the societal impact of algorithmic systems.
  • Students can describe how societal contexts, such as Nordicness, impacts algorithmic systems.

Syllabus

Lecture 1: Defining algorithmic systems

Additional readings:

  • Ananny, M. (2016). Toward an Ethics of Algorithms. Science, Technology, & Human Values, 41(1), 93–117. https://doi.org/10.1177/0162243915606523
  • Gillespie, T. (2014). The Relevance of Algorithms. In Media Technologies (pp. 167–194). The MIT Press. https://doi.org/10.7551/mitpress/9780262525374.003.0009

Lecture 2: Methods to study algorithmic systems

Read before class:

  • Kitchin, R. (2017). Thinking critically about and researching algorithms. Information, Communication & Society, 20(1), 14–29. https://doi.org/10.1080/1369118X.2016.1154087

Additional readings

  • Light, B., Burgess, J., & Duguay, S. (2018). The Walkthrough Method: An Approach to the Study of Apps. New Media & Society, 20(3), 881–900. https://doi.org/10.1177/1461444816675438
  • DeVos, A., Dhabalia, A., Shen, H., Holstein, K., & Eslami, M. (2022). Toward User-Driven Algorithm Auditing: Investigating users’ strategies for uncovering harmful algorithmic behavior. CHI Conference on Human Factors in Computing Systems, 1–19. https://doi.org/10.1145/3491102.3517441
  • Shen, H., Devos, A., Eslami, M., & Holstein, K. (2021). Everyday Algorithm Auditing: Understanding the Power of Everyday Users in Surfacing Harmful Algorithmic Behaviors. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), 29. https://doi.org/10.1145/3479577

Lecture 3: Stakeholders and algorithms

Everyone reads

  • Burrell, J., & Fourcade, M. (2021). The Society of Algorithms. Annual Review of Sociology, 47(1), annurev-soc-090820-020800. https://doi.org/10.1146/annurev-soc-090820-020800

“Users” – read at least one

  • Burrell, J., Kahn, Z., Jonas, A., & Griffin, D. (2019). When users control the algorithms: Values expressed in practices on the twitter platform. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW). https://doi.org/10.1145/33592407
  • Ytre-Arne, B., & Moe, H. (2021). Folk theories of algorithms: Understanding digital irritation. Media, Culture and Society, 43(5), 807–824. https://doi.org/10.1177/0163443720972314

”Developers” – read at least one

  • Vakkuri, V., Kemell, K. K., Jantunen, M., & Abrahamsson, P. (2020). “This is Just a Prototype”: How Ethics Are Ignored in Software Startup-Like Environments. Lecture Notes in Business Information Processing, 383 LNBIP(Profes 2019), 195–210. https://doi.org/10.1007/978-3-030-49392-9_13
  • Passi, S., & Barocas, S. (2019). Problem formulation and fairness. FAT* 2019 - Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency, 39–48. https://doi.org/10.1145/3287560.3287567

Lecture 4: Metaphors of/for algorithmic systems

  • Pääkkönen, J., Nelimarkka, M., Haapoja, J., & Lampinen, A. (2020). Bureaucracy as a Lens for Analyzing and Designing Algorithmic Systems. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–14. https://doi.org/10.1145/3313831.3376780
  • Haapoja, J., Laaksonen, S. M., & Lampinen, A. (2020). Gaming Algorithmic Hate-Speech Detection: Stakes, Parties, and Moves. Social Media and Society, 6(2). https://doi.org/10.1177/2056305120924778

Additional readings

  • Ammitzbøll Flügge, A., Hildebrandt, T., & Møller, N. H. (2021). Street-Level Algorithms and AI in Bureaucratic Decision-Making. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1–23. https://doi.org/10.1145/3449114
  • Alkhatib, A., & Bernstein, M. (2019). Street-Level Algorithms. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI ’19, 1–13. https://doi.org/10.1145/3290605.3300760

Lecture 5: Game building

Reading:

  • Dumit, J. (2017). Game Design as STS Research. Engaging Science, Technology, and Society, 3, 603. https://doi.org/10.17351/ests2017.132

Lecture 6: Algorithmic systems, society and values

Readings:

  • Birhane, A., Kalluri, P., Card, D., Agnew, W., Dotan, R., & Bao, M. (2022). The Values Encoded in Machine Learning Research. 2022 ACM Conference on Fairness, Accountability, and Transparency, 22, 173–184. https://doi.org/10.1145/3531146.3533083
  • Scharlach, R., Hallinan, B., & Shifman, L. (2023). Governing principles: Articulating values in social media platform policies. New Media & Society, 146144482311565. https://doi.org/10.1177/14614448231156580
  • Grön, K., & Nelimarkka, M. (2020). Party Politics, Values and the Design of Social Media Services. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2), 1–29. https://doi.org/10.1145/3415175

Additional readings:

  • Nissenbaum, H. (2005). Values in Technical Design. In C. Mitcham (Ed.), Encyclopedia of Science, Technology, and Ethics (pp. lxvi–lxx). MacMillan.