HAAS: A Policy-Aware Framework for Adaptive Task Allocation Between Humans and Artificial Intelligence Systems
Vicente Pelechanoa, Antoni Mestre, Manoli Albert, Miriam Gil
TLDR
HAAS is a framework for adaptive human-AI task allocation, combining rule-based governance with a contextual-bandit learner to optimize collaboration.
Key contributions
- Introduces HAAS, a framework for adaptive human-AI task allocation using rules and contextual bandits.
- Shows governance is a tunable design variable, converting AI autonomy into supervised collaboration.
- Finds stronger governance can boost manufacturing performance and reduce fatigue simultaneously.
- Demonstrates moderate governance becomes competitive as the AI learner gains experience.
Why it matters
This paper offers a novel framework for dynamically distributing work between humans and AI, moving beyond binary choices. It provides a pre-deployment workbench to inspect and compare human-AI allocation policies, crucial for organizational design. Its findings challenge assumptions about governance as pure overhead, highlighting its potential for performance gains.
Original Abstract
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 coupled components: a rule-based expert system that enforces governance constraints before any learning occurs, and a contextual-bandit learner that selects among feasible collaboration modes from outcome feedback. Task-agent fit is represented through five auditable cognitive dimensions and a five-mode autonomy spectrum -- from human-only to fully autonomous -- embedded in a reproducible benchmark spanning both domains. Three empirical findings emerge. First, 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. Second, in manufacturing, stronger governance can improve operational performance and reduce fatigue simultaneously -- a workload-buffering effect that contradicts the usual framing of governance as pure overhead. Third, no single governance setting dominates across all contexts; moderate governance becomes increasingly competitive as the learner accumulates experience within the governed action space. Together, these findings position HAAS as a pre-deployment workbench for comparing and inspecting human--AI allocation policies before organisational commitment.
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