pith:3VPBJN6P
HAAS: A Policy-Aware Framework for Adaptive Task Allocation Between Humans and Artificial Intelligence Systems
HAAS shows governance constraints act as tunable variables that shift AI tasks toward supervised human collaboration with measurable domain effects.
arxiv:2605.02832 v2 · 2026-05-04 · cs.AI · cs.HC · cs.SE
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Claims
Governance is not a binary switch but a tunable design variable: tighter constraints predictably convert autonomous AI assignments into supervised collaborations, with domain-specific costs and benefits; in manufacturing, stronger governance can improve operational performance and reduce fatigue simultaneously.
The five auditable cognitive dimensions and five-mode autonomy spectrum accurately represent task-agent fit across the tested domains, and the benchmark results generalize beyond the specific software engineering and manufacturing scenarios used.
HAAS is an implemented framework using rule-based governance and contextual bandits to adapt human-AI task allocation, with empirical results showing tunable governance can improve manufacturing performance and reduce fatigue.
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| First computed | 2026-05-20T00:03:13.818596Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
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| Schema | pith-number/v1.0 |
Canonical hash
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Canonical record JSON
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