Causal Decomposition and Counterfactual Auditing for Ethical AI-Driven Adaptive Leadership in Organisational Transformation
Keywords:
Causal Artificial Intelligence (Causal AI), Ethical AI, Organizational Leadership, Counterfactual Reasoning, Structural Causal Models (SCMs)Abstract
We propose a novel framework, the Causal Ethical AI Leadership Framework (CEALF), that integrates structural causal models and counterfactual reasoning into the decision-making loop of AI-driven leadership during organisational transformation. The core motivation is to address the ethical risk that AI-suggested leadership actions may inadvertently exploit hierarchical power dynamics to manipulate employee incentives, thereby undermining genuine performance drivers. The methodology comprises four interconnected modules: a Causal Reward Decomposition Engine that estimates path-specific effects to isolate manipulative pathways from legitimate motivational ones; a Variational Disentanglement Module that separates confounding power dynamics from authentic performance factors using a beta-variational autoencoder; a Path-Specific Counterfactual Auditor that computes counterfactual harm scores by comparing observed rewards against ethically neutral baselines; and a Do-Calculus Intervention Simulator that selects optimal interventions by maximising expected reward while minimising identified harm. These modules operate as a real-time governance layer, receiving inputs from conventional employee sentiment and performance systems and outputting ethically validated recommendations to human leaders. The principal contribution is the introduction of path-specific counterfactual analysis as an operational tool for ethical auditing of AI leadership, moving beyond static ethical checklists to a dynamic, causally-aware architecture. Furthermore, the framework provides a principled method for decomposing reward signals into ethically legitimate and manipulative components, thereby enabling continuous recalibration of leadership strategies. The significance of this work lies in its potential to align AI-driven organisational change with multi-stakeholder well-being, offering a scalable solution for ethical governance in increasingly automated management systems.
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