Truth machines: synthesizing veracity in AI language models

Luke Munn, Liam Magee, Vanicka Arora

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

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. This article discusses the struggle for truth in AI systems and the general responses to date. 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.

Original languageEnglish
Article numbere11510
Pages (from-to)2759-2773
Number of pages15
JournalAI and Society
Volume39
Issue number6
DOIs
Publication statusPublished - Dec 2024

Bibliographical note

Publisher Copyright:
© The Author(s) 2023.

Open Access - Access Right Statement

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Keywords

  • Veracity
  • InstructGPT
  • ChatGPT
  • AI
  • Large language model
  • Truthfulness
  • GPT-3

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