Expectation Inflation of Artificial Intelligence: When Human Demands on AI Exceed Moral Design Capacity

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What happens when the ethical and interpretive demands placed on artificial intelligence (AI) exceed what AI systems can legitimately support? This paper develops Expectation Inflation as a conceptual framework for explaining how AI-related ethical risk can arise not only from technical failures but also from escalating human and organisational expectations and the over-delegation of moral and interpretive authority. Adopting a conceptual and normative-analytical approach, the study synthesises interdisciplinary literature and constructs a theoretical model rather than seeking empirical validation. Drawing on Science and Technology Studies (STS), anthropomorphism, automation reliance, algorithmic authority, human–AI collaboration, and AI ethics, the paper conceptualises Expectation Inflation as an escalation process in which functional confidence can develop into epistemic and ultimately moral expectations. The Expectation Inflation Curve captures this progression through three stages: Rational Delegation, Normative Drift, and Moral Substitution. The paper further introduces Moral Design Capacity (MDC) as a context-dependent normative boundary beyond which the moral and interpretive authority attributed to an AI system becomes disproportionate to its demonstrated capabilities, decision context, and surrounding governance safeguards. To address this misalignment, the paper proposes Expectation Governance, comprising Delegation Boundaries, Expectation Auditing, and Moral Accountability Indexing. The framework complements existing approaches to responsible AI by shifting analytical attention from system-side properties alone to the demand side of AI governance: how organisations construct, amplify, and institutionalise expectations about what AI should know, interpret, and morally decide. The study thereby provides a theoretical basis for examining expectation–capacity misalignment in organisational and institutional settings and establishes propositions and governance mechanisms for subsequent empirical investigation.