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.
Hutchins, and David Kirsh
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 5verdicts
UNVERDICTED 5roles
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background 2representative citing papers
AI coding tools divide into collaborators that initiate most PRs and assistants that support human-led ones, yet humans retain merge authority across all five tools examined.
Mixed-Initiative Context reconceptualizes interaction context as a dynamic, jointly manageable structure that humans and AI can actively organize according to task needs.
PrivacyAkinator uses LLM-generated questions grounded in data-flow representations and a news-mined design space to help developers surface privacy decisions, yielding 47% more decisions identified in 73% less time than PRAM in a 24-person study.
Shorter LLM response latencies reduce perceived output thoughtfulness and usefulness, while task type affects prompting frequency independently of latency.
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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Collaborator or Assistant? How AI Coding Agents Partition Work Across Pull Request Lifecycles
AI coding tools divide into collaborators that initiate most PRs and assistants that support human-led ones, yet humans retain merge authority across all five tools examined.
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Mixed-Initiative Context: Structuring and Managing Context for Human-AI Collaboration
Mixed-Initiative Context reconceptualizes interaction context as a dynamic, jointly manageable structure that humans and AI can actively organize according to task needs.
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PrivacyAkinator: Articulating Key Privacy Design Decisions by Answering LLM-Generated Multiple-choice Questions
PrivacyAkinator uses LLM-generated questions grounded in data-flow representations and a news-mined design space to help developers surface privacy decisions, yielding 47% more decisions identified in 73% less time than PRAM in a 24-person study.
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The Impact of Response Latency and Task Type on Human-LLM Interaction and Perception
Shorter LLM response latencies reduce perceived output thoughtfulness and usefulness, while task type affects prompting frequency independently of latency.