Explainable AI Models for Ensuring Transparency in CPG Markets Pricing and Promotions

Abstract

The increasing reliance on artificial intelligence (AI) for pricing and promotional decisions in the consumer-packaged goods (CPG) industry has amplified concerns about algorithmic transparency, fairness, and regulatory compliance. This paper explores the role of Explainable Artificial Intelligence (XAI) in enhancing the interpretability and accountability of AI-driven pricing systems. Using a conceptual and analytical approach, it synthesizes current literature on AI-powered pricing, XAI methodologies such as SHAP and LIME, and evolving legal frameworks governing algorithmic decision-making. The study compares interpretability techniques, highlights their suitability for CPG applications, and discusses organizational and regulatory implications of adopting transparent AI models. Findings indicate that explainability fosters greater managerial trust, consumer confidence, and compliance readiness, while reducing the risks of bias and reputational harm. The paper concludes with recommendations for integrating XAI from inception, establishing governance protocols, and balancing predictive accuracy with interpretability. Future research directions include causal explainability, real-time transparency, and sustainability of AI-driven promotional systems.