Between Model and World: Trust, Transparency, and Theory of Mind in AI Systems

Abstract: Models both represent and act upon the world, their effects shaped by data selection and algorithmic design. The inherent constraints of translating social phenomena into computable form limit the possibility of a stable ground truth. By the time large language models (LLMs) emerged publicly in 2022, the field had already confronted issues of bias, representativeness, and fidelity rooted in earlier supervised learning systems such as ImageNet. The shift to unsupervised, large-scale training in generative AI (GenAI) has further obscured these debates, replacing explicit concern with “ground truth” by expansive claims about “machine intelligence.” This paper looks at issues of trust, transparency, and theory of mind (ToM) in AI Systems as situated and contingent, inseparable from the contexts in which it is produced and applied (Jaton, 2021; Kang, 2023). As such concepts such as “ground truth” or “synthetic” reflect procedural decisions that predefine what counts as truth within algorithmic systems. Key questions include: How do models both represent and reshape the worlds they model? What conditions determine what counts as trustworthy in automated systems? Do practices of data collection, design, and validation produce epistemic hierarchies or a machinic theory of mind?

Bio: Dr. Beth Coleman is director of the Knowledge Media Design Institute (KMDI), a multidisciplinary research institute supported by the Faculty of Information at the University of Toronto. An Associate Professor of Data & Cities at the Institute of Communication, Culture, Information and Technology and Faculty of Information, Coleman works in the disciplines of science and technology studies and generative aesthetics with research focuses on artificial intelligence & smart technology, urban data and civic engagement, and transmedia arts. She is the author of Hello Avatar (MIT press) and Reality Was Whatever Happened: Octavia Butler AI and Other Possible Worlds (K. Verlag). She has been a Google Brain and Responsible AI senior visiting researcher as well as a 2021 Google Artists and Machines Intelligence awardee. She is a research lead on AI policy and praxis at the Schwartz Reisman Institute for Technology & Society. Her research affiliations have included the Berkman Klein Center for Internet & Society, Harvard University; Microsoft Research New England; Data & Society Institute, New York; and expert consultant for the European Commission Digital Futures. She is currently working on the monograph, AI in the World: Perils and Possibilities of a General Purpose Technology.

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