The Philosophy of Science Group at the Department of Philosophy cordially invites you to this mini workshop. (Please note that the order of the presentations has changed.) Best, Tarja Knuuttila * * *Mini workshop on AI and computing — 20.05.2025* Lecture Room 3D (Room D0316, 3rd floor) Universitätsstraße 7, 1010 Vienna Organized by: Univ.-Prof. Tarja Knuuttila 17:00 -18:00 Dr. Nick Wiggershaus (University of Lille) * * *Computational Artifacts and the Problem of Creation* As computer science integrates principles from logic, engineering, and physics, the ontological status of its core entities, such as computer programs, remains contested. Programs are often characterized as hybrids that have a “dual nature.” In attempts to untangle such hybrids, philosophers of computing have applied the concept of ‘technical artifact’ (combining teleological function and physical structure) to computing. While productive, it overlooks a notorious problem from the philosophy of art: the /Problem of Creation/, which asks how abstract objects like musical works or novels can be brought into existence through concrete human activity. I argue that, like repeatable artworks, computational artifacts have different representational modes (e.g., symbolic, mathematical, diagrammatic) and implementational media (e.g., ink on paper, chalk on a whiteboard, electrical signals, punched cards, etc.). Just as a novel or a musical work is not identical to any one performance or copy, a computer program persists across implementations. This invites a philosophical conundrum: How can programmers /create /abstract objects that are not located in space or time? By appropriating solutions to the Problem of Creation, we gain alternative ways to characterize the ontological status of programs and other computing objects. I conclude by exploring whether we can understand computational artifacts as /abstract /technical artifacts. 18:15-19:15 Dr. Laura Savolainen (University of Helsinki) *Emperor’s New Crowds: “Untrustworthy” Workers and “Ground Truth”* Ground-truth datasets are supposed to nail down facts about the “world” represented by data, so that machine learning models trained on them will behave reliably in that same world. Yet when annotation is outsourced to platform workers whom engineers do not know, and often mistrust, how is such reliability achieved or even imagined? Based on 27 interviews with machine learning researchers and practitioners, this paper investigates how ground-truth datasets are stabilised when 1) annotators are positioned as unreliable non-experts, 2) recognised domain experts are prohibitively expensive, and 3) the platform architecture itself suppresses deliberation, feedback, and learning. Given these constraints, I illustrate ground-truthing as a canny, iterative practice shaped by task design choices, aggregation methods, disciplinary conventions, and the affective politics of trusting data supplied by unknown workers. Rather than reflecting the world, the resulting datasets operationalize narrowly bounded problem formulations that satisfy performance goals ‘well enough’ for downstream modelling. By analysing the epistemic hierarchies, organizational constraints and judgment calls embedded in these pipelines, the discussion offers a concrete case for re-evaluating realist assumptions about data, evidence, and representation in contemporary AI research. Moreover, the analysis opens normative space for re-imagining data pipelines around more transparent authority structures and richer human feedback for more reliable processes and outputs.