The effects of domain knowledge on trust in explainable AI and task performance: A case of peer-to-peer lending
Bibliographic record
Abstract
Increasingly, artificial intelligence (AI) is being used to assist complex decision-making such as financial investing. However, there are concerns regarding the black-box nature of AI algorithms. The field of explainable AI (XAI) has emerged to address these concerns. XAI techniques can reveal how an AI decision is formed and can be used to understand and appropriately trust an AI system. However, XAI techniques still may not be human-centred and may not support human decision-making adequately. In this work, we explored how domain knowledge, identified by expert decision makers, can be used to achieve a more human-centred approach to AI. We measured the effect of domain knowledge on trust in AI, reliance on AI, and task performance in an AI-assisted complex decision-making environment. In a peer-to-peer lending simulator, non-expert participants made financial investments using an AI assistant. The presence or absence of domain knowledge was manipulated. The results showed that participants who had access to domain knowledge relied less on the AI assistant when the AI assistant was incorrect and indicated less trust in AI assistant. However, overall investing performance was not affected. These results suggest that providing domain knowledge can influence how non-expert users use AI and could be a powerful tool to help these users develop appropriate levels of trust and reliance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".