mlsanityData to 7 Oct 2026

Value-Sensitive Delegation in Everyday AI Agent Use: Evidence from OpenClaw

arXiv 2609.22067 ↗First posted 18 Sep 2026cs.HC cs.AIcited by 0

On the map: LLM agents and tool use → Social simulation and multi-agent debate also fits: Agent threats, guardrails and governance

Abstract (arXiv)

Users increasingly delegate work to autonomous AI agents, yet evaluations typically measure task completion rather than the values users prioritize. Using Value Sensitive Design, we analyzed, with LLM assistance, 73,093 first-person Reddit posts about using OpenClaw, each for its human value, agent aspect, value fulfillment, and user outcome. The 21 values form six value groups, including Autonomous, Dependable, and Affordable Operation, Bounded Reach, Reviewability, and Equitable Access. Relative to each aspect's corpus share, values clustered not at the agent's outputs but at the operating conditions users set around a run. Values were usually met where users described what the agent delivered, in five of six groups, and mostly unmet where users described supervising it, in all six groups. We conceptualize this pattern as value-sensitive delegation. Supporting human values requires attention not only to what an agent accomplishes, but to the conditions users set around delegation, including cost, access, and oversight.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 24 of its 109 references that are in the map. First posted on arXiv 18 Sep 2026.

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24 of 109 references.

References · 24

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12 more
  1. 2022Evaluation Gaps in Machine Learning Practicecited by 48
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  6. 2024ValueCompass: A Framework for Measuring Contextual Value Alignment Between Human and LLMs
  7. 2026Toward a Theory of Value in AI Alignment
  8. 2026"What Did It Actually Do?": Understanding Risk Awareness and Traceability for Computer-Use Agents