A Comprehensive Framework for Human - AI Collaborative Decision Making in Intelligent Retail Environments

Abstract

Artificial intelligence (AI) approaches have been more and more adopted in the retail industry in the past years, ranging from demand forecasting, dynamic pricing, inventory optimization to personalization of recommendations and promotions. However, conventional AI-centric decision platforms are often limited in interpretability, unable to manage data heterogeneity across channels, real-time adaptability and lack of domain knowledge from human expertise. Intelligent retailing is one application field that this paper would propose a human-AI cooperative decision-making system in order to combine the benefits of human expertise and machine learning. This system should be developed on: (i) modular architecture that includes a reinforcement learning (RL) core, fuzzy logic reasoning engine, human feedback interface, bias detection module; (ii) explainable AI (XAI) methods to output the rationale of the model, and also have human operators for (iii) human-in-the-loop correction and (iv) bias mitigation and fairness checks, and (v) a hybrid multi-store evaluation mechanism. Experiment: we compare our framework against baselines such as traditional rule-based systems, pure RL models and the more recent hybrid human-AI methods. Experiments are based on six months of transaction and inventory data from three separate mid-size retail stores (> 500,000 transactions, ∼2,000 SKUs), with results showing an increase of 15 percent in revenue and 10–12 percent reduction in stock-outs, and an average increase of around 18 percent in staff satisfaction indices, and with decision latency below 200 ms. The advantage can be shown by paired t-tests (ANOVA, p = 0.05). Ablation experiments demonstrate the importance of each of the modules (e.g., XAI transparency, fuzzy logic smoothing, bias detector). The qualitative interview data with store managers on the explanations and override controls provide a basis for trust.