Algorithm Pricing and Algorithm Biases in Retail: A Systematic Review
Keywords:
Algorithm pricing, algorithm biases, distrimination , ethical practisesAbstract
Objectives: This review aims to analyse existing literature on the various algorithmic pricing strategies used in retail environments. It focuses on how these algorithms can unintentionally reinforce biases and discusses effective ways to avoid or manage such biases in retail. Prior Work: As retailers adopt more advanced algorithms, the risk of unintentional and often discriminatory biases becomes an important concern. Approach: A systematic review of literature was conducted using the PRISMA protocol, with data sourced from the Scopus and Web of Science databases. Results: Literature documents dynamic and personalised pricing as major forms of algorithmic pricing in retail, where prices are differentiated according to customer behaviour, market and customer data. Algorithm biases stem from input data biases, model design biases, and contextual and application biases. Value: The study recommends careful examination of input data, transparency, explainability and human oversight to manage algorithmic biases in retail effectively.
References
Abhadiomhen, S. E., & Oyibo, K. (2025). Algorithmic Bias in Algorithm-Driven User Interfaces: Recommendations for Fairness. In BCSS (pp. 125-130).
Akter, S., Dwivedi, Y. K., Biswas, K., Michael, K., Bandara, R. J., & Sajib, S. (2021). Addressing algorithmic bias in AI-driven customer management. Journal of Global Information Management (JGIM), 29(6), 1-27.
Anica-Popa, I., Anica-Popa, L., Rădulescu, C., & Vrîncianu, M. (2021). The integration of artificial intelligence in retail: benefits, challenges and a dedicated conceptual framework. Amfiteatru Economic, 23(56), 120-136.
Aparicio, D., & Misra, K. (2023). Artificial intelligence and pricing. Emerald Publishing Limited.
Aparicio, D., Metzman, Z., & Rigobon, R. (2024). The pricing strategies of online grocery retailers. Quantitative Marketing and Economics, 22(1), 1-21.
Bahangulu, J. K., & Owusu-Berko, L. (2025). Algorithmic bias, data ethics, and governance: Ensuring fairness, transparency and compliance in AI-powered business analytics applications. World Journal of Advanced Research and Reviews, 25(2), 1746-1763.
Dubus, A. (2024). Behavior-based algorithmic pricing. Information Economics and Policy, 66, 101081.
Garbarino, E., & Maxwell, S. (2010). Consumer response to norm-breaking pricing events in e-commerce. Journal of Business Research, 63(9-10), 1066-1072.
Ho, T., Dang, H., Bui, M., Nguyen, N., Dinh, T., & Thai, H. (2026). Cognitive multi-agent pricing: an enhanced deep reinforcement learning framework for dynamic SKU competition in E-commerce. Journal of Revenue and Pricing Management, 1-18.
Hufnagel, G., Schwaiger, M., & Weritz, L. (2022). Seeking the perfect price: Consumer responses to personalized price discrimination in e-commerce. Journal of Business Research, 143, 346-365.
Jakkula, A. R. (2023). Integrating AI in e-commerce platforms: Exploring the future of shopping. Journal of Technological Innovations, 4(1).
Wu, J., & Zhao, C. (2023). The Online Retailer’s Randomized Pricing Strategy to Compete With an Offline Retailer, International Journal of Electronic Commerce, 27(2), 210-235, DOI: 10.1080/10864415.2023.2184239
Kittipanya-ngam, P., Tan, K. H., & Cavite, H. J. (2025). Future-ready AI: A framework for ethical and sustainable adoption. Technology in society, 83, 102993.
Kordzadeh, N., & Ghasemaghaei, M. (2022). Algorithmic bias: review, synthesis, and future research directions. European Journal of Information Systems, 31(3), 388-409.
Lee, S., Illia, A., & Lawson‐Body, A. (2011). Perceived price fairness of dynamic pricing. Industrial Management & Data Systems, 111(4), 531-550.
Lu, Y., Yan, X., & Yu, Y. (2026). Multiproduct Dynamic Pricing with Popularity Bias. Production and Operations Management, 10591478261424035.
Moussawi, S., Deng, X., & Joshi, K. D. (2024). AI and discrimination: Sources of algorithmic biases. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 55(4), 6-11.
Nunan, D., & Di Domenico, M. (2022). Value creation in an algorithmic world: Towards an ethics of dynamic pricing. Journal of Business Research, 150, 451-460.
Qiao, W., Wood, L. C., Tang, S., Teng, Z., & Huang, M. (2026). Fairness-Constrained Dynamic Pricing via Shielded Deep Reinforcement Learning. Mathematics, 14(4), 600.
Roselli, D., Matthews, J., & Talagala, N. (2019, May). Managing bias in AI. In Companion proceedings of the 2019 world wide web conference (pp. 539-544).
Shapiro, A. (2024). Price monitoring, or where surveillance studies meets the sociology of price formation. Surveillance & Society, 22(3), 248-262.
Spann, M., Bertini, M., Koenigsberg, O., Zeithammer, R., Aparicio, D., Chen, Y., ... & Yoo, H. (2025). Algorithmic pricing: Implications for marketing strategy and regulation. International Journal of Research in Marketing.
Tang, C. S., Yoo, O. S., & Zhan, D. (2021). Time-based pricing at grocery stores? Equilibrium transition under retail competition and negative congestion externality. Equilibrium Transition under Retail Competition and Negative Congestion Externality (July 30, 2021).
Tetteh, F. K., Kwateng, K. O., & Mensah, J. (2024). Green logistics practices: A bibliometric and systematic methodological review and future research opportunities. Journal of Cleaner Production, 476, 143735.
Vuyyuru, S. R. (2025). Understanding The Ethical Challenges Of AI In Retail And Addressing Data Privacy, Algorithmic Bias And Consumer Trust. International Journal of Engineering Development and Research, 13(2), 100-106.
Wamsler, J., Natter, M., & Algesheimer, R. (2022). Transitioning to dynamic prices: Should pricing authority remain with the company or be delegated to the service employees instead? Journal of Business Research, 139, 1476-1488.
Wu, J., & Zhao, C. (2023). The online retailer’s randomized pricing strategy to compete with an offline retailer. International Journal of Electronic Commerce, 27(2), 210-235.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Lilian Gumbo
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The author fully assumes the content originality and the holograph signature makes him responsible in case of trial.
