Explainable Multi-Agent Budget Optimization for Privacy-Preserving Creator Economy Advertising in AI-Driven Digital Platforms (International Journal of Artificial Intelligence Engineering and Systems, 2026)
The rapid expansion of the creator economy within AI-driven digital platforms has introduced critical challenges at the intersection of advertising effectiveness, user privacy, and algorithmic fairness. This paper proposes a comprehensive framework for explainable multi-agent budget optimization tailored to privacy-preserving creator economy advertising. The framework integrates multiple intelligent agents responsible for budget allocation, attribution modeling, privacy budget management, and explainability generation, operating under federated and differentially private constraints. We examine the structural trade-offs inherent in balancing ad performance with creator monetization, user data protection, and platform governance. A key contribution is the design of a hierarchical multi-agent architecture that enables decentralized decision-making while preserving global optimization objectives through coordinated budget negotiations. The paper analyzes the role of differential privacy budgets in cross-device attribution and the implications of adaptive privacy mechanisms for fairness across creators of varying scales. Explainability is achieved through agent-level audit trails and counterfactual explanations that allow stakeholders to interpret budget reallocation decisions. We further explore deployment considerations including scalability under heterogeneous device environments, robustness against adversarial attacks, and sustainability of privacy guarantees over long-term learning horizons. Policy implications are discussed with respect to regulatory compliance, creator equity, and transparency obligations. By bridging explainable AI, multi-agent systems, and privacy-preserving techniques, this work provides a systematic framework for designing trustworthy advertising ecosystems in the creator economy.
Keywords: multi-agent systems, budget optimization, privacy preservation, creator economy, explainable AI, differential privacy, federated learning, advertising attribution, fairness, digital platform governance