Journal of Advances in Developmental Research

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Explainable AI Toolkits for Trustworthy Retail Decision-Making Systems

Author(s) Udit Agarwal
Country United States
Abstract The integration of Artificial Intelligence (AI) algorithms into the retail sector—encompassing areas such as dynamic pricing, hyper-personalization, and inventory management—has revolutionized efficiency but concurrently introduced significant ethical and technical challenges stemming from the "black-box" nature of complex machine learning models. This paper reviews the necessity and implementation of Explainable Artificial Intelligence (XAI) toolkits as a strategic imperative for establishing Trustworthy AI (TAI) systems in retail. XAI is defined as a set of processes crucial for human comprehension and trust in algorithmic outputs, distinguishing it from mere interpretability by focusing on the rationale of how decisions are reached. Trustworthiness is analyzed through established pillars, including robustness, transparency, accountability, and fairness. A conceptual synthesis of two dominant model-agnostic XAI toolkits—SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME)—demonstrates their utility in detecting and mitigating algorithmic bias, particularly in hyper-personalization and pricing strategies where fairness deficits can lead to consumer harm. Finally, the paper discusses critical deployment challenges, notably the trade-off between model accuracy and interpretability. The conclusion posits that XAI toolkits are essential for providing the auditability and transparency required for the responsible, compliant, and sustainable deployment of AI in high-stakes retail operations.
Keywords Explainable AI (XAI), Trustworthy AI (TAI), Retail Decision-Making, SHAP, LIME, Algorithmic Fairness, Transparency, Hyper-Personalization.
Field Engineering
Published In Volume 17, Issue 1, January-June 2026
Published On 2026-01-28
Cite This Explainable AI Toolkits for Trustworthy Retail Decision-Making Systems - Udit Agarwal - IJAIDR Volume 17, Issue 1, January-June 2026. DOI 10.71097/IJAIDR.v17.i1.1680
DOI https://doi.org/10.71097/IJAIDR.v17.i1.1680
Short DOI https://doi.org/hbm79s
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