Truth Machines: Synthesizing Veracity in AI Language Models

University of Stirling, United Kingdom vanicka.arora@stir.ac.uk Abstract As AI technologies are rolled out into healthcare, academia, human resources, law, and a multitude of other domains, they become de-facto arbiters of truth. But truth is highly contested, with many different definitions and approaches. It then investigates the production of truth in InstructGPT, a large language model, highlighting how data harvesting, model architectures, and social feedback mechanisms weave together disparate understandings of veracity. It conceptualizes this performance as an operationalization of truth, where distinct, often conflicting claims are smoothly synthesized and confidently presented into truth-statements. We argue that these same logics and inconsistencies play out in Instruct's successor, ChatGPT, reiterating truth as a non-trivial problem. We suggest that enriching sociality and thickening "reality" are two promising vectors for enhancing the truth-evaluating capacities of future language models. We conclude, however, by stepping back to consider AI truth-telling as a social practice: what kind of "truth" do we as listeners desire? OpenAI's latest language model appeared to We stress then that truth in AI is not just technical but be powerful and almost magical, generating news articles, also social, cultural, and political, drawing on particular writing poetry, and explaining arcane concepts norms and values. But a week later, the coding the technical matters: translating truth theories into site StackOverflow banned all answers produced actionable architectures and processes updates them by the model. "The primary problem," explained in significant ways. These disparate sociotechnical the staff, "is that while the answers which ChatGPT forces coalesce into a final AI model which purports produces have a high rate of being incorrect, they typically to tell the truth--and in doing so, our understanding look like they might be good and the answers of "truth" is remade. "The ideal of truth is a fallacy are very easy to produce" (Vincent 2022). For a site for semantic interpretation and needs to be changed," aiming to provide correct answers to coding problems, suggested two AI researchers (Welty and Aroyo 2015).