To believe or not to believe? Generative AI and the 'trust dilemma'in charitable and medical crowdfunding
Abstract
While charitable and medical crowdfunding has become a vital resource for individuals facing financial hardships, it also raises significant ethical and trust-related concerns. Generative AI (GenAI) offers opportunities to enhance fundraising effectiveness, but its misuse could undermine donor confidence and exacerbate existing trust issues. Grounded in trust theory and algorithm aversion, this research examines the complexities of integrating GenAI into crowdfunding. Study 1 employed paragraph vectors, the NRC Emotional Lexicon, and LIWC to analyze linguistic and sentiment differences between human-written and AI-rewritten GoFundMe campaigns (N = 1,800). Study 2 used a human-in-the-loop approach in a between-subjects randomised experiment (N = 601) to assess how AI authorship and disclosure influenced cognitive and affective trust among donors. Findings revealed that AI-rewritten messages were more analytical, goal-directed, and emotionally expressive but appeared overly polished and less authentic. Notably, disclosing AI involvement significantly reduced both cognitive and affective trust. This ‘trust dilemma’ underscores the ethical complexities of GenAI in crowdfunding, emphasising the need for transparency and regulatory frameworks to ensure responsible AI integration in socially sensitive domains.
Study specs
- Authors
- C Arnold,LZ Xu,K Saffarizadeh
- Discipline
- Human-Computer Interaction,Behavioral Science
- Year
- 2021
- Human Data Platform
- Prolific
- Source
- View Source Google Scholar
Peer Review & Critical Discussion
Potential Selection Bias in 2023 Cohort
The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.
Non-naive Participants Issue
I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.
RLHF Applicability to This Study Design
The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.
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