Truth-tracking with non-expert information sources

Singleton, Joseph and Booth, Richard ORCID: https://orcid.org/0000-0002-6647-6381 2024. Truth-tracking with non-expert information sources. Journal of Artificial Intelligence Research 81 , pp. 619-641.

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Abstract

We study what can be learned when receiving propositional reports from multiple nonexpert information sources. We suppose that sources report all that they consider possible, given their expertise. This may result in false and inconsistent reports when sources lack expertise on a topic. A learning method is truth-tracking, roughly speaking, if it eventually converges to correct beliefs about the “actual” world. This involves finding both the actual state of affairs in the domain described by the sources, and finding the extent of the expertise of the sources themselves. We investigate the extent to which truth-tracking is possible, and describe what information can be learned even if the actual world cannot be pinned down uniquely. We find that a broad spread of expertise among the sources allows the actual state of affairs to be found, even if no individual source is an expert on all topics. On the other hand, narrower expertise at the individual level allows the actual expertise to be found more easily. Finally, we turn to learning methods themselves: we provide a postulate-based characterisation of truth-tracking for general methods under mild assumptions, before looking at a couple of specific classes of methods from the belief change literature.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	AI Access Foundation
ISSN:	1076-9757
Date of First Compliant Deposit:	20 September 2024
Last Modified:	04 Feb 2025 14:33
URI:	https://orca.cardiff.ac.uk/id/eprint/172282

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