Can AI Extract Antecedent Factors of Human Trust in AI? An Application of Information Extraction for Scientific Literature in Behavioural and Computer Sciences
This work addresses the challenge of structuring complex trust factors for AI applications, but it is incremental as it focuses on dataset creation and benchmarking in a specific domain.
The study tackled the problem of extracting antecedent factors of human trust in AI from scientific literature by creating the first annotated English dataset and benchmarking state-of-the-art methods, finding that supervised learning is necessary as prompt-based LLMs are not currently feasible for this task.
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.