The khipu problem frames a governance failure in distributed AI where interpretive continuity is lost even when traces remain, requiring infrastructure to preserve reading practices rather than only data retention.
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7 Pith papers cite this work, alongside 1,594 external citations. Polarity classification is still indexing.
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Pith papers citing it
1,594
external citations · OpenAlex
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2026 7roles
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The paper defines five AI system categories for public administration and reports that 55% of 91 recent papers leave the system type underspecified while 31% study one type but motivate with another.
DAISY is a structured form tool that generates more complete AI disclosure statements for research papers without reducing author comfort levels.
Decommissioned AI systems produce residual socio-technical risks termed AI debris that continue to influence institutions, addressed via a proposed AI Debris Decommissioning Protocol.
Comparative analysis of China's and the UK's algorithm registers reveals that design choices shape registers into pre-market approval systems, ecosystem-monitoring tools, or expandable regulatory infrastructure—not just transparency mechanisms.
Imbalanced user-AI relationships form a distinct front-end ethical failure in healthcare AI that design choices such as restricted inputs and suppressed uncertainty can undermine agency and that reciprocity offers a path to more balanced interactions.
Audit of two German algorithm registers using checklists from a 2025 proposal finds they require adaptations to meet proposed transparency goals.
citing papers explorer
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The Khipu Problem: Institutional Legibility Under Distributed Cognition
The khipu problem frames a governance failure in distributed AI where interpretive continuity is lost even when traces remain, requiring infrastructure to preserve reading practices rather than only data retention.
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A Technical Typology of AI Systems in Public Administration
The paper defines five AI system categories for public administration and reports that 55% of 91 recent papers leave the system type underspecified while 31% study one type but motivate with another.
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AI Disclosure with DAISY
DAISY is a structured form tool that generates more complete AI disclosure statements for research papers without reducing author comfort levels.
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AI Debris: Residual Risk and the Afterlife of Failed AI Systems
Decommissioned AI systems produce residual socio-technical risks termed AI debris that continue to influence institutions, addressed via a proposed AI Debris Decommissioning Protocol.
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Understanding the Role of Algorithm Registers in AI Governance Through Comparative Analysis of China and the UK
Comparative analysis of China's and the UK's algorithm registers reveals that design choices shape registers into pre-market approval systems, ecosystem-monitoring tools, or expandable regulatory infrastructure—not just transparency mechanisms.
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The Imbalanced User-AI Relationships as an Ethical Failure of Front-End Design in Healthcare AI
Imbalanced user-AI relationships form a distinct front-end ethical failure in healthcare AI that design choices such as restricted inputs and suppressed uncertainty can undermine agency and that reciprocity offers a path to more balanced interactions.
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Are Algorithm Registers Transparent? Perspectives from Germany
Audit of two German algorithm registers using checklists from a 2025 proposal finds they require adaptations to meet proposed transparency goals.