The role of review bodies in AI contestation in fostering customer trust

The growing integration of artificial intelligence (AI) into high-risk sectors such as finance and public administration has raised urgent questions about how individuals can contest AI-driven decisions. While regulatory frameworks emphasize contestability as a core principle for promoting fairness and accountability, there remains little practical guidance on how to implement effective contestation mechanisms. This research explores how customers perceive different types of contestation review bodies, human, AI, and hybrid models, and examines the impact of these structures on judgments of fairness, empathy, and trust toward organizations. Additionally, it investigates whether the location of the review body, whether internal to the organization or external and independent, influences customer perceptions. Drawing from research on consumer complaint behavior and organizational trust, the study addresses critical gaps between regulatory intentions and real-world practices. The findings will offer practical insights for businesses seeking to design contestation processes that enhance accountability and customer satisfaction, particularly as AI-driven service models expand. Moreover, this research will inform policymakers aiming to develop more detailed, sector-specific guidelines that ensure the principle of contestability is operationalized effectively. This research contributes to advancing responsible AI governance by providing evidence-based recommendations on structuring review processes that protect stakeholder and foster long-term trust in AI-enabled decision-making systems.