Collaborative Human-AI Decision Frameworks for Responsible Outcomes

Abstract

Collaborative human-AI decision-making -- structured processes in which human judgement and AI analytical capabilities are integrated to produce decisions that neither could achieve alone -- represents a promising paradigm for responsible AI deployment in high-stakes domains. Unlike human-in-the-loop architectures focused on oversight of AI outputs, collaborative frameworks treat human and AI as complementary cognitive agents with distinct capabilities, seeking to harness cognitive complementarity while managing the risks of automation bias, deskilling, and responsibility diffusion. This paper proposes the Collaborative Human-AI Decision (CHAD) framework, a structured methodology for designing, evaluating, and governing collaborative human-AI decision processes in high-stakes contexts. The CHAD framework comprises five components: capability mapping, complementarity optimisation, authority allocation, conflict resolution, and outcome accountability. The framework is evaluated through a mixed-methods study combining expert validation (n = 32) and a field experiment in three organisational settings -- clinical oncology decision-making, financial credit review, and urban planning -- involving 108 professional decision-makers. Field experiment results demonstrate that CHAD-structured collaboration achieves a 27.4% improvement in decision quality (measured against gold-standard outcomes; SD = 4.8%), a 34.1% improvement in decision confidence calibration (SD = 5.3%), and a 31.6% improvement in perceived decision responsibility (SD = 4.9%) compared to unstructured human-AI collaboration. The study contributes the CHAD framework specification, a Collaboration Quality Assessment (CQA) instrument, and empirical evidence that structured collaborative frameworks produce superior responsible outcomes compared to unstructured human-AI pairing.