The Trust Paradox in AI–Blockchain Systems: Exploring Transparency, Immutability, and Human Trust
TL;DR
The study argues that trustworthy AI–Blockchain systems require more than technological guarantees: they require understandable explanations, appropriate human oversight, data governance, accountability mechanisms, and context-sensitive transparency.
Abstract
Artificial Intelligence (AI) and Blockchain have emerged as two influential technologies in the development of modern digital systems. AI provides intelligent prediction, automation, and decision-making capabilities, while Blockchain offers decentralization, traceability, transparency, and resistance to unauthorized modification. Because of these complementary characteristics, the integration of AI and Blockchain is increasingly presented as a way to create more secure and trustworthy digital environments. However, an important question remains: does greater technical transparency and immutability necessarily result in greater human trust? This paper explores this question through a qualitative thematic analysis of existing research on AI trust, algorithmic transparency, explainability, Blockchain immutability, accountability, and AI–Blockchain integration. Twenty relevant research works were examined to identify recurring concepts, opportunities, limitations, and contradictions. The analysis reveals a trust paradox: Blockchain can increase confidence in the integrity and history of recorded information, but its immutability does not automatically establish trust in the quality of the underlying data or the decisions produced by AI. Similarly, making an AI system more transparent does not necessarily make it more understandable or trustworthy to ordinary users. Four major themes emerged from the literature: technical transparency, perceived human trust, accountability and explainability, and the limitations of immutability. The study argues that trustworthy AI–Blockchain systems require more than technological guarantees. They require understandable explanations, appropriate human oversight, data governance, accountability mechanisms, and context-sensitive transparency. The paper concludes by proposing a conceptual trust framework that connects Blockchain integrity with AI explainability and human-centered governance.
