Governance of AI Applications in High-Risk Scenarios A Risk Analysis Based on the ``Cognitive Compensation'' Perspective

Abstract

In the era of intelligent transformation, the widespread application of AI systems in high-risk scenarios (such as autonomous driving, medical diagnosis, and judicial sentencing) has triggered unprecedented governance challenges. Traditional governance frameworks often focus on the “technical reliability” of AI or the “ethical review” of humans, failing to deeply reveal the root causes of risks in human-machine collaborative decision-making. This paper introduces the “Theory of Cognitive Compensation” and the “RID Cognitive Dynamics Model” from “Knowing and Speaking”, constructing a systematic analytical framework for AI application risks in high-risk scenarios. The study proposes that the essence of AI application is human beings transferring part of the “Structural Generation (I)” function to machines to alleviate cognitive “Demand/Drive (D)”, forming a “Cognitive Compensation” mechanism. However, when this compensation mechanism is improperly extended to high-risk scenarios, it induces five major structural risks: concept drift, automation bias, responsibility suspension, cognitive hallucination, and malicious compensation. To address these risks, this paper proposes establishing a “Dynamic Redundancy” governance framework, including structural redundancy, cognitive redundancy, institutional redundancy, and fault-tolerant redundancy, to ensure human beings’ ultimate control over high-risk decisions. By critically analyzing the EU’s Artificial Intelligence Act and the US governance model, this paper argues that the core principle of high-risk scenario governance is to strictly delineate compensation boundaries and achieve “responsibility centralization”, thereby providing a new theoretical foundation and institutional design reference for global AI governance.