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Course pages 2024–25

Theories of Socio-digital Design for Human Centred AI

Preliminary reading

Students are encouraged to read Alan Blackwell’s Moral Codes: Designing Alternatives to AI) as a preliminary reading to the course.

Reading list - Part 1

Seminar 1: Responsible AI and HCI: Insights from Human-Computer Interaction and Critical Design for Human-Centred Artificial Intelligence

Essential reading:

Essential practical/technical resources:

Further reading:

  • Qian Yang, Aaron Steinfeld, Carolyn Rosé, and John Zimmerman. 2020. Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to Design. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI '20). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3313831.3376301
  • Anthony Dunne and Fiona Raby. Speculative Everything: Design, Fiction, and Social Dreaming. The MIT Press, 2013. Project MUSE http://muse.jhu.edu/book/28148

Exercise:

  • Product design ideation (and justifying the use of AI)

Seminar 2: Intersectional Design: Reorienting AI Development Towards Social Justice

Essential reading:

  • Sasha Costanza-Chock, Design Justice, A.I., and Escape from the Matrix of Domination. Journal of Design and Science (2018), https://doi.org/10.21428/96c8d426
  • Cynthia L. Bennett and Daniela K. Rosner. 2019. The Promise of Empathy: Design, Disability, and Knowing the "Other". In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19). Association for Computing Machinery, New York, NY, USA, Paper 298, 1–13. https://doi.org/10.1145/3290605.3300528

Essential practical/technical resources:

Further reading:

Exercise:

Seminar 3: Participatory design and co-design: On Power-sensitive Inclusion and Collaboration in AI Development

Essential reading:

  • Fernando Delgado, Stephen Yang, Michael Madaio, Qian Yang, The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice, https://doi.org/10.48550/arXiv.2310.00907
  • Bergman, S., Marchal, N., Mellor, J. et al. STELA: a community-centred approach to norm elicitation for AI alignment. Sci Rep 14, 6616 (2024). https://doi.org/10.1038/s41598-024-56648-4
  • [SHORT READ] Mona Sloane, Emanuel Moss, Olaitan Awomolo, and Laura Forlano. 2022. Participation Is not a Design Fix for Machine Learning. In Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO '22). Association for Computing Machinery, New York, NY, USA, Article 1, 1–6. https://doi.org/10.1145/3551624.3555285

Further reading:

  • King, Paula & Cormack, Donna. (2023). Indigenous Peoples, Whiteness, and the Coloniality of Co-design. coloniality-of-co-design.pdf
  • Blakeley H. Payne, Jordan Taylor, Katta Spiel, and Casey Fiesler. 2023. How to Ethically Engage Fat People in HCI Research. In Companion Publication of the 2023 Conference on Computer Supported Cooperative Work and Social Computing (CSCW '23 Companion). Association for Computing Machinery, New York, NY, USA, 117–121. https://doi.org/10.1145/3584931.3606987
  • Hollanek, T., Ganesh, I. (2024). Easy Wins and Low Hanging Fruit. Blueprints, Toolkits, and Playbooks to Advance Diversity and Inclusion in AI. Institute of Network Cultures. https://doi.org/10.17863/CAM.113869

Exercise:

  • Identifying primary and secondary stakeholders, and developing a stakeholder engagement strategy

Seminar 4: More-than-human Design: Considering the Environment and Non-human Animals as Stakeholders in Design

Essential reading:

Essential practical/technical resources:

Further reading:

Exercise:

  • Team project presentations


Reading list - Part 2

Seminar 5: The EU AI Act in Design Practice: A Risk-based Approach.

Essential reading:

Essential practical/technical resources:

Exercise:

  • Classifying AI systems under the EU AI Act categorisation + AI risk assessment

Seminar 6: AI and Interface Design: Transparency and Disclosure

Essential reading:

  • Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20(3), 973-989. https://doi.org/10.1177/1461444816676645
  • Bogucka, E., Šćepanović, S., & Quercia, D. (2024). Atlas of AI Risks: Enhancing Public Understanding of AI Risks. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 12(1), 33-43. https://doi.org/10.1609/hcomp.v12i1.31598

Essential practical/technical resources:

Further reading:

  • Arianna Rossi and Monica Palmirani. 2020. Can Visual Design Provide Legal Transparency? The Challenges for Successful Implementation of Icons for Data Protection, Design Issues 36 (3), https://doi.org/10.1162/desi_a_00605

Exercise:

  • AI disclaimers: communicating risks to users

Seminar 7: AI and Interface Design: Consent, Complaint, Contestation

Essential reading:

Exercise:

  • Developing a system for lodging and responding to user complaints

Seminar 8: AI Ethics Practical Resources: Questioning the Usefulness and Usability of Ethical Design Toolkits

Essential reading:

  • Richmond Y. Wong, Michael A. Madaio, and Nick Merrill. 2023. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proc. ACM Hum.-Comput. Interact. 7, CSCW1, Article 145 (April 2023), https://doi.org/10.1145/3579621
  • Widder, D. G., & Nafus, D. (2023). Dislocated accountabilities in the “AI supply chain”: Modularity and developers’ notions of responsibility. Big Data & Society, 10(1). https://doi.org/10.1177/20539517231177620

Essential practical/technical resources:

Further reading:

  • Os Keyes, Jevan Hutson, and Meredith Durbin. 2019. A Mulching Proposal: Analysing and Improving an Algorithmic System for Turning the Elderly into High-Nutrient Slurry. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (CHI EA '19). Association for Computing Machinery, New York, NY, USA, Paper alt06, 1–11. https://doi.org/10.1145/3290607.3310433
  • Hollanek, T. The ethico-politics of design toolkits: responsible AI tools, from big tech guidelines to feminist ideation cards. AI Ethics (2024). https://doi.org/10.1007/s43681-024-00545-z

Exercise:

  • Building your own ‘AI ethics toolkit’