Can AI Extract Antecedent Factors of Human Trust in AI? An Application of Information Extraction for Scientific Literature in Behavioural and Computer Sciences

• URL: http://arxiv.org/abs/2412.11344v1
• Date: Mon, 16 Dec 2024 00:02:38 GMT
• Title: Can AI Extract Antecedent Factors of Human Trust in AI? An Application of Information Extraction for Scientific Literature in Behavioural and Computer Sciences
• Authors: Melanie McGrath, Harrison Bailey, Necva Bölücü, Xiang Dai, Sarvnaz Karimi, Cecile Paris,
• Abstract summary: Trust in AI is where factors contributing to human trust in AI applications are studied. With the input of domain experts, we create the first annotated English dataset in this domain. We benchmark it with state-of-the-art methods using large language models in named entity and relation extraction. Our results indicate that this problem requires supervised learning which may not be currently feasible with prompt-based LLMs.
• Score: 9.563656421424728
• License:
• Abstract: Information extraction from the scientific literature is one of the main techniques to transform unstructured knowledge hidden in the text into structured data which can then be used for decision-making in down-stream tasks. One such area is Trust in AI, where factors contributing to human trust in artificial intelligence applications are studied. The relationships of these factors with human trust in such applications are complex. We hence explore this space from the lens of information extraction where, with the input of domain experts, we carefully design annotation guidelines, create the first annotated English dataset in this domain, investigate an LLM-guided annotation, and benchmark it with state-of-the-art methods using large language models in named entity and relation extraction. Our results indicate that this problem requires supervised learning which may not be currently feasible with prompt-based LLMs.

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