The Hidden Cost of Convenience: How Algorithmic Pricing Hurts Vulnerable Consumers

Oluwafemi Akinwande

doi:10.56201/wjimt.v9.no11.2025.pg154.163

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The Hidden Cost of Convenience: How Algorithmic Pricing Hurts Vulnerable Consumers

Oluwafemi Akinwande

Abstract

It has become a concern in the global business circle over the rise of algorithmic pricing and Artificial Intelligence (AI)-driven personalized pricing models which has redesigned digital commerce, allowing industries and organizations to adjust prices dynamically and drastically. Consequent upon the fact that these models rely on consumer data, urgency, and behavioral triggers, they often result in price discrimination and reduced market transparency thereby hurting vulnerable consumers. Algorithmic price calculation is by far one of the most remarkable features of the present-day consumer economy, which to an extraordinarily makes use of data to discover market conditions and the individual consumers behavioral attitudes. The increasingly refined forms of data-driven pricing have been met with anxiety in the e-commerce and business public debate and remain highly puzzling from a legal and an economic perspective. Although, the associated concerns do not emanate merely from the novelty of algorithmic pricing. They are also indicative of the new types of economic and social perils that this way of price setting entails (or is likely to entail). Algorithmic price discrimination is one of the most prominent manifestations of data-driven pricing and raises challenging regulations and policy questions. The article is an exposition for policy makers to act because algorithmic pricing is instantly humanizing issue; vulnerable consumers could mean you, your parent, your neighbor, and names the villains subtly with systems that optimize profit while deepening inequality. This study situates the researcher as a consumer advocate and not just a techie, and deep moral and public-interest orientation— a key EB1A credibility marker.

Keywords

Algorithmic Pricing; Benefits; E-Commerce; Harm; Vulnerable Consumers

References

Ariel E. & Maurice E. S. (2020). Sustainable and Unchallenged Algorithmic Tacit Collusion, NW. J. TECH. & INTELL. PROP. 217, 226. Ariel, E. & Maurice, E. S. (2017). Artificial Intelligence & Collusion: When Computers Inhibit Competition, 2017 U. ILL. L. REV. 1775, 1780. Bar-Gill, O., Sunstein, C. R., & Talgam-Cohen, I. (2023). Algorithmic harm in consumer markets. Journal of Legal Analysis, 15(1), 1-47.mmgv Ezrachi & Stucke, supra note 2, at 1784–89 (describing scenarios where firms could use pricing algorithms to explicitly collude); Harrington, supra note 7, at 346 (“[P]ricing algorithms . . . are rich enough to encompass the collusive strategies that have been used by human agents.)” Franck, C., Johan, H. & Hans, K. (2018). The Technologies of Price Display: Mundane Retail Price Governance in the Early Twentieth Century, 47 ECON. & SOC’Y 572, 579–80. Grochowski, M., Jabłonowska, A., Lagioia, F., & Sartor, G. (2022). Algorithmic Price Discrimination and Consumer Protection. Technology and Regulation, 2022 (Special Issue: Should Data Drive Private Law?), 36-47. Hugh, R. (2008). Price Controls, in the Concise Encyclopedia of Economics 409, 409 (David R. Henderson ed.) Kamraju, M. (2024). Algorithmic Pricing and Consumer Vulnerability: The Ethical and Economic Implications of Artificial Intelligence (AI)-Driven Pricing Models. ASEAN Journal of Economic and Economic Education, 3(2), 201-208. Kleinberg, Jon et al. 2018a. Algorithmic Fairness. 108 AEA Papers Proc. 22–27. MacKay, A., & Weinstein, S. N. (2022). Dynamic pricing algorithms, consumer harm, and regulatory response. Wash. UL Rev., 100, 111. Neel, M., Parth, D. & Aditya, A. (2018). Amazon Changes Prices on Its Products About Every 10 Minutes – Here’s How and Why They Do It, BUS. INSIDER. Accessed from https://www.businessinsider.com/amazon-price-changes. Paterson, J. M. et al. (2021). The Hidden Harms of Targeted Advertising by Algorithm and Interventions from the Consumer Protection Toolkit. 9 Int. J. Consumer Law & Practice 1–17. Slaughter, R. K. (2021). Algorithms and Economic Justice: A Taxonomy of Harms and a Path Forward for the Federal Trade Commission. 23 Yale J. Law Technol. S1. Stephanie, C. (2021). Market Share of Leading Retail E-Commerce Companies in the United States as of October 2021, STATISTA (Oct. 13, 2021) https://www.statista.com/statistics/274255/marketshare-of-the-leading-retailers-in-us-e- commerce/ [https://perma.cc/L7VT-MQ42].

The Hidden Cost of Convenience: How Algorithmic Pricing Hurts Vulnerable Consumers

Abstract

Keywords

References

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