Los riesgos regulatorios y de mercado relacionados con la fijación de precios por algoritmos en el comercio electrónico: Una guía para la evaluación de los riesgos por parte de los inversores

The rapid adoption of AI-driven pricing models in e-commerce has revolutionized how businesses optimize revenue, but it has also introduced a labyrinth of regulatory and market risks. For investors, navigating these challenges requires a nuanced understanding of evolving legal frameworks, algorithmic vulnerabilities, and compliance obligations. As 2025 unfolds, the intersection of AI, antitrust law, and consumer protection has become a critical focal point for due diligence.

Regulatory Risks: A Shifting Legal Landscape

State-level legislation in the United States has taken a proactive stance against algorithmic pricing practices perceived to undermine competition or exploit consumer data. New York's Algorithmic Pricing Disclosure Act, for instance, mandates that businesses disclose when prices are set using algorithms trained on personal data, with noncompliance risking fines of up to $1,000 per violation. This law reflects a broader trend of transparency requirements, as seen in California's Assembly Bill No. 325, which amended the Cartwright Act to prohibit algorithms trained on nonpublic competitor data and lowered pleading standards for antitrust complaints.

Meanwhile, New York's Section 340-b extends liability beyond traditional antitrust norms by banning algorithmic rent-setting tools that perform a "coordinating function," even without explicit collusion between competitors. These measures signal a regulatory shift toward preemptively addressing algorithmic collusion, a concept that challenges the federal Sherman Act's reliance on proving direct agreements.

Globally, the EU AI Act imposes stricter obligations on high-risk systems, including dynamic pricing algorithms. Under this framework, AI systems that adjust prices based on consumer behavior or sensitive data must undergo certification and adhere to GDPR-compliant data governance. Noncompliance risks fines of up to 7% of global turnover, as demonstrated by recent penalties against tech giants like Amazon and Google for data privacy violations.

Market Risks: From Hallucinations to Collusion

Beyond regulatory scrutiny, AI-driven pricing models expose investors to market risks tied to algorithmic unpredictability. Generative AI systems, for example, are prone to "hallucinations"-outputs that appear credible but are factually incorrect or legally problematic. In e-commerce, this could manifest as erroneous price adjustments or misleading product descriptions, eroding customer trust and brand value.

Algorithmic volatility also poses systemic risks. When pricing models autonomously respond to market signals, they can inadvertently trigger price wars or manipulate supply-demand dynamics, akin to high-frequency trading in financial markets. Such behaviors attract regulatory attention, particularly under frameworks like the EU AI Act, which emphasize accountability for high-risk systems.

A more insidious risk lies in algorithmic collusion. Systems designed to "match competitor prices minus a percentage" could violate antitrust laws by enabling tacit coordination without human intent. While enforcement against self-learning AI remains nascent, the potential for unintended collusion underscores the need for rigorous oversight.

Investor Due Diligence: Frameworks and Checklists

To mitigate these risks, investors must adopt AI-specific due diligence frameworks. Key considerations include:
1. Data Provenance and Governance: Assess whether training data is ethically sourced, licensed, and free from biases that could lead to discriminatory pricing according to AI due diligence checklists.
2. Algorithmic Transparency: Verify that pricing models can explain their decision-making processes, particularly under laws like New York's disclosure requirements as mandated by state law.
3. Compliance Certifications: For EU investments, ensure alignment with the AI Act's high-risk system certification process and GDPR obligations.
4. Scenario Testing: Evaluate how algorithms respond to edge cases, such as sudden market shocks or competitor price changes, to identify unintended behaviors.

Investors are increasingly leveraging AI-powered due diligence tools to automate these assessments. These tools aggregate real-time data, analyze compliance risks, and flag anomalies in pricing algorithms. For example, predictive analytics can model the financial impact of regulatory penalties, such as the €746 million fine imposed on Amazon for GDPR violations.

Case Studies: Lessons from Non-Compliance

Recent enforcement actions highlight the financial and reputational stakes of algorithmic missteps. In 2025, Amazon faced a €746 million penalty for using AI-driven ad targeting tools that violated GDPR, underscoring the EU's enforcement rigor. Similarly, Google's €50 million fine in France for invalid consent in data processing illustrates the importance of user-centric compliance.

In the U.S., startups failing to comply with New York's disclosure laws risk swift penalties. For instance, a hypothetical e-commerce platform using personalized pricing algorithms without proper disclosures could face class-action lawsuits under the state's civil penalties framework. These cases emphasize the need for proactive compliance, particularly in sectors like real estate and retail, where algorithmic pricing is pervasive.

Conclusion: Balancing Innovation and Accountability

For investors, the key to navigating algorithmic pricing lies in balancing innovation with accountability. While AI offers transformative potential, its deployment must align with regulatory expectations and market realities. By integrating AI-driven due diligence tools, prioritizing transparency, and staying abreast of legislative trends, investors can mitigate risks while capitalizing on the efficiency gains of algorithmic pricing.

As the regulatory landscape continues to evolve, due diligence must extend beyond technical assessments to encompass strategic alignment with ESG goals and long-term market resilience. In an era where algorithms shape both prices and perceptions, the stakes for investors have never been higher.