Position: Human-Centric AI Requires a Minimum Viable Level of Human Understanding
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915766944661504 |
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| author | Lin, Fangzhou Ge, Qianwen Xu, Lingyu Li, Peiran Gao, Xiangbo Xing, Shuo Yamada, Kazunori Zhang, Ziming Zhang, Haichong Tu, Zhengzhong |
| author_facet | Lin, Fangzhou Ge, Qianwen Xu, Lingyu Li, Peiran Gao, Xiangbo Xing, Shuo Yamada, Kazunori Zhang, Ziming Zhang, Haichong Tu, Zhengzhong |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_00854 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Position: Human-Centric AI Requires a Minimum Viable Level of Human Understanding Lin, Fangzhou Ge, Qianwen Xu, Lingyu Li, Peiran Gao, Xiangbo Xing, Shuo Yamada, Kazunori Zhang, Ziming Zhang, Haichong Tu, Zhengzhong Artificial Intelligence 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. |
| title | Position: Human-Centric AI Requires a Minimum Viable Level of Human Understanding |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2602.00854 |