Truth-tracking with Non-expert Information Sources

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.

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Publication Details

Journal
Journal of Artificial Intelligence Research
Published
2024-11-24
DOI
https://doi.org/10.1613/jair.1.15273
Primary Topic
Machine Learning and Algorithms
Type
article
Field-Weighted Citation Impact
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article

Truth-tracking with Non-expert Information Sources

Joseph Singleton, Richard Booth
Journal of Artificial Intelligence Research
Machine Learning and Algorithms
article

Truth-tracking with Non-expert Information Sources

Joseph Singleton, Richard Booth
article en

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.

Journal of Artificial Intelligence ResearchVol. 81
Cardiff University (GB)
Peace, Justice and strong institutions
Openalex Percentile: Top 81%
Machine Learning and Algorithms
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