Position: Human-Centric AI Requires a Minimum Viable Level of Human Understanding

2026-01-31

AI Summary

As AI systems grow increasingly capable, human ability to understand, verify, and intervene in their operation diminishes, undermining the effectiveness of oversight. This work proposes the “Cognitive Integrity Threshold” (CIT)—a formalized boundary defining the minimal level of human understanding required to sustain meaningful supervision, autonomy, and accountability in human–AI collaboration. Building on three interrelated dimensions—verifiability, understanding-preserving interaction, and institutional governance—the study develops an operational framework that integrates insights from human–computer interaction, cognitive science, and AI governance. Moving beyond conventional approaches centered on transparency and control, this research offers a novel paradigm for human-centered AI design in accountability-sensitive contexts, ensuring that human oversight remains substantive rather than merely symbolic as automation advances.

Technology Category

• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Humans and AI: Intelligent User Interfaces
• Intelligent Robots: Human-Robot Interaction

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

AI systems increasingly produce fluent, correct, end-to-end outcomes. Over time, this erodes users'ability to explain, verify, or intervene. We define this divergence as the Capability-Comprehension Gap: a decoupling where assisted performance improves while users'internal models deteriorate. This paper argues that prevailing approaches to transparency, user control, literacy, and governance do not define the foundational understanding humans must retain for oversight under sustained AI delegation. To formalize this, we define the Cognitive Integrity Threshold (CIT) as the minimum comprehension required to preserve oversight, autonomy, and accountable participation under AI assistance. CIT does not require full reasoning reconstruction, nor does it constrain automation. It identifies the threshold beyond which oversight becomes procedural and contestability fails. We operatinalize CIT through three functional dimensions: (i) verification capacity, (ii) comprehension-preserving interaction, and (iii) institutional scaffolds for governance. This motivates a design and governance agenda that aligns human-AI interaction with cognitive sustainability in responsibility-critical settings.

Problem

• Capability-Comprehension Gap
• Cognitive Integrity Threshold
• human-AI interaction
• oversight
• accountability

Innovation

• Cognitive Integrity Threshold
• Capability-Comprehension Gap
• human-AI interaction
• accountable AI
• cognitive sustainability