Register here by 25.8.2026

Time: 12:15-15:45 EET, Wednesday, August 26th, 2026

Place: Hybrid – Room 251, Startup Factory, Yliopistonranta 10, 2nd Floor, University of Vaasa

Programme

12:15-12:30 Opening words – Rebekah Rousi

12:30-13:00 Irina Shklovski, University of Copenhagen – Why can’t we get it right? The challenges and limits of “Responsible” AI

13:00-13:15 BUGGED shorts I – case Vastaamo (Satu Rantakokko and Tinja Myllyviita)

13:15-13:30 Coffee

13:30-14:00 Susanna Paasonen, University of Turku – Discomfort, attachment and boundary work

14:00-14:15 BUGGED shorts II – playing with privacy (Ville Vakkuri and Emmanuel Anti)

14:15-14:45 Emma Pretty, Tampere University – When the System Reads You: Ethics of Physiologically Adaptive Systems

14:45-15:00 BUGGED shorts III – social media, politics and dual standards (Krista Penttilä, Tinja Myllyviita & Rebekah Rousi)

15:00-15:10 Short Break

15:10-15:45 Final project reflections, outputs, achievements and discussions with collaborators

Synopses:

Why can’t we get it right? The challenges and limits of “Responsible” AI

Irina Shklovski, University of Copenhagen

In 1987 Robert Kraut, then a computer scientist at Bell Labs, asked how can technology be designed “to exploit its usefulness without exploiting its users.” Nearly four decades later, we still don’t have an answer. Over the years arguments about technology have moved from concerns about data and privacy, to bias and discrimination in algorithmic systems, to ethical concerns data driven AI systems. The current solution seems to be “Responsible AI” – comprising of tools, methods, checklists, standards, compliance evaluations, and ways of thinking. Yet AI systems continue to fail us, plagued by the same problems of bias, privacy concerns, and overhyped promises that consistently fall short. Why do problematic AI systems seem unavoidable and what does it take to create AI “responsibly”? I will focus on the problems of data quality, when and how technical challenges become ethical concerns, and the limits of “being ethical” when developing AI.

Discomfort, attachment and boundary work

Susanna Paasonen, University of Turku

During the past decade or so, the promise of social media to connect people with frictionless ease has been met with scholarship exploring mundane tactics and possibilities of disconnection (Light 2014; Karppi 2018) and the persistent discomfort, or friction, involved in what Elija Cassidy identifies as “participatory reluctance”, namely engagement “when we would actually prefer not to or would rather do so under altered circumstances” (Cassidy 2016: 2614). Building on findings from the large-scale research project, Intimacy in Data Driven Culture (2019–2025), this talk explores reluctant and ambiguous engagement in a context where social media, and networked connectivity more broadly, has become infrastructural to how everyday lives are navigated and how social proximities and distances are managed. Lauren Berlant’s (2022: 36) discussion of “the inconvenience paradox of dependency” of “needing people or a situation and hating to have that need” further helps in mapping out the social frictions involved. In platformed contexts, such inconvenient dependencies further extend to information design and data policies impossible for users to influence, breeding both apprehension and tactical boundary work in terms of visibility, participation and engagement.

When the System Reads You: Ethics of Physiologically Adaptive Systems

Emma Pretty, Tampere University

Adaptive systems in games, training, and social platforms use physiological signals to personalise the experience, or are positioned to do so in the near future. A person may consent to data collection and understand what a system can infer from their heart rate or performance. What is less known to the user, what the system is trying to achieve by adapting to them, and what the consequences of that adaptation might be. This talk considers transparency about system goals and the interests driving adaptation.