Human Collaboration in Compensation Decision Making

Compensation decisions are simultaneously data-intensive, high-stakes, and deeply contextual. This paper develops a governance-ready framework for human–AI collaboration in compensation decision-making that positions AI as an advisory system augmenting rather than replacing professional judgment. The framework integrates five layers—Strategy & Guardrails, Data & Privacy, Models & Explainability, Decision Workflows (Human-in-the-Loop), and Governance & Assurance—linked by a closed-loop learning cycle for continuous improvement. We articulate expected impacts (decision quality, consistency, equity, efficiency), managerial and regulatory implications (including pay-transparency readiness), and a conceptual figure that translates design principles into practice. Research objectives and questions are specified to enable empirical evaluation via staged rollouts with fairness diagnostics and explainability embedded. We discuss limitations (data quality, subgroup sizes, change management, and cross-border comparability), propose a pragmatic methodology (stepped-wedge design, difference-in-differences, fairness metrics, power analysis, and measurement-error mitigation), and outline expected outcomes and contributions. The approach provides a scalable blueprint for global organizations to strengthen trust and performance while maintaining equity and compliance under evolving disclosure norms.