{"paper":{"title":"HAAS: A Policy-Aware Framework for Adaptive Task Allocation Between Humans and Artificial Intelligence Systems","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"HAAS shows governance constraints act as tunable variables that shift AI tasks toward supervised human collaboration with measurable domain effects.","cross_cats":["cs.HC","cs.SE"],"primary_cat":"cs.AI","authors_text":"Antoni Mestre, Manoli Albert, Miriam Gil, Vicente Pelechano","submitted_at":"2026-05-04T17:09:21Z","abstract_excerpt":"Deciding how to distribute work between humans and AI systems is a central challenge in organisational design. Most approaches treat this as a binary choice, yet the operational reality is richer: humans and AI routinely share tasks or take complementary roles depending on context, fatigue, and the stakes involved. Governing that distribution -- balancing efficiency, oversight, and human capability -- remains an open problem. This paper presents Human-AI Adaptive Symbiosis (HAAS), an implemented framework for adaptive task allocation in software engineering and manufacturing. HAAS combines two"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"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.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"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.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"HAAS shows governance constraints act as tunable variables that shift AI tasks toward supervised human collaboration with measurable domain effects.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"838745a1d75577e01cc71bc604a9818a2f7e673be05ef6739031e7c1709b2fba"},"source":{"id":"2605.02832","kind":"arxiv","version":2},"verdict":{"id":"dc1c8c97-eea7-45e1-b663-65594a1d5424","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-08T18:00:00.668923Z","strongest_claim":"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.","one_line_summary":"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.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"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.","pith_extraction_headline":"HAAS shows governance constraints act as tunable variables that shift AI tasks toward supervised human collaboration with measurable domain effects."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.02832/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_compliance","ran_at":"2026-05-19T15:56:31.707851Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"91fa5ab17bde80a5cf6ac36f6e183efa696fa5efcee7267a426e5b4bbb40d4dd"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":3,"snapshot_sha256":"473a0522ef8b8703a7e4422df3781355dedc33478cc9bf3148c0f77be8da7fbf"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}