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Do large language models have a legal duty to tell the truth?

Abstract:
Careless speech is a new type of harm created by large language models (LLM) that poses cumulative, long-term risks to science, education and shared social truth in democratic societies. LLMs produce responses that are plausible, helpful and confident, but that contain factual inaccuracies, misleading references and biased information. These subtle mistruths are poised to cumulatively degrade and homogenize knowledge over time. This article examines the existence and feasibility of a legal duty for LLM providers to create models that ‘tell the truth’. We argue that LLM providers should be required to mitigate careless speech and better align their models with truth through open, democratic processes. We define careless speech against ‘ground truth’ in LLMs and related risks including hallucinations, misinformation and disinformation. We assess the existence of truth-related obligations in EU human rights law and the Artificial Intelligence Act, Digital Services Act, Product Liability Directive and Artificial Intelligence Liability Directive. Current frameworks contain limited, sector-specific truth duties. Drawing on duties in science and academia, education, archives and libraries, and a German case in which Google was held liable for defamation caused by autocomplete, we propose a pathway to create a legal truth duty for providers of narrow- and general-purpose LLMs.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1098/rsos.240197

Authors

More by this author
Institution:
University of Oxford
Division:
SSD
Department:
Oxford Internet Institute
Role:
Author
More by this author
Institution:
University of Oxford
Division:
SSD
Department:
Oxford Internet Institute
Role:
Author
ORCID:
0000-0002-4709-6404
More by this author
Institution:
University of Oxford
Division:
SSD
Department:
Oxford Internet Institute
Role:
Author


Publisher:
Royal Society
Journal:
Royal Society Open Science More from this journal
Volume:
11
Issue:
8
Article number:
240197
Publication date:
2024-08-07
Acceptance date:
2024-05-17
DOI:
EISSN:
2054-5703


Language:
English
Keywords:
Pubs id:
1997819
Local pid:
pubs:1997819
Deposit date:
2024-05-21
ARK identifier:

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