Machine learning and predictive algorithms find patterns in large stores of data and make predictions which corporations and governments use to support decision-making. Yet, the system's representation of reality can be more influential to outcomes than the complexities of daily life. They become problematic when they undermine the inclusivity of public decision making, and when their use perpetuates social or economic inequality. To address these challenges, the public must be able to participate in discourse about the implications of algorithmic systems. I propose a series of participatory installations exploring the impacts of algorithmic systems, providing contexts for active exploration of these concerns. I will conduct phenomenographic interviews to better understand how visitors experience art installations about technical topics, providing insight for subsequent installations. I will consolidate the results into a set of best practices about engaging the public on these topics.
Kathryn Blair, Pil Hansen, Lora Oehlberg. Participatory Art for Public Exploration of Algorithmic Decision-Making. In Proceedings of the ACM on Designing Interactive Systems Conference (DIS '21). ACM, New York, NY, USA Page: 1-4. DOI: 10.1145/3461778.3462068