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

TL;DR

This paper introduces the concept of the Capability-Comprehension Gap in AI, emphasizing the need for a minimum human understanding level, called the Cognitive Integrity Threshold, to ensure effective oversight and accountability.

Contribution

It formalizes the Cognitive Integrity Threshold (CIT) as a foundational concept for maintaining human oversight in AI systems and operationalizes it through verification, interaction, and governance dimensions.

Findings

Defines the Capability-Comprehension Gap in AI systems.

Introduces the Cognitive Integrity Threshold (CIT) as a minimum understanding requirement.

Proposes a design and governance framework to support cognitive sustainability.

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.