An international technology company implemented an AI-powered dynamic pricing system to optimize product prices across multiple markets.

The system automatically adjusted prices by analyzing competitors’ pricing, customer behavior, inventory levels and market conditions.

Initially, the company experienced increased sales and improved profitability. However, concerns later emerged that certain customer groups had been offered different prices and that the algorithm had produced outcomes capable of restricting competition in specific markets.

Following an investigation by the relevant regulatory authority, the company struggled to explain how the algorithmic decisions had been made, incurred substantial legal defense costs and suffered significant reputational damage.

Key Risk: The issue was not the use of artificial intelligence itself. The problem was that the algorithmic decision-making process had not been assessed in advance from the perspectives of competition law and legal compliance.

A customer service company implemented an AI-based system to analyze call recordings and customer communications.

The objective was to improve customer experience and increase operational efficiency.
However, it was later discovered that the datasets used to train the system had not been sufficiently anonymized and that certain personal data had been processed without explicit consent or another valid legal basis.

Following complaints from customers, the data protection authority initiated an investigation, audited the company’s data processing activities and considered a range of regulatory sanctions.

In addition, customers brought claims seeking both material damages and compensation for non-pecuniary harm.

Key Risk: The issue was not the use of artificial intelligence. The problem was the failure to assess whether the data used to train the AI system had been collected and processed in compliance with applicable data protection laws.

A media and content company began using AI-powered image and text generation tools for its marketing activities.

The AI-generated content quickly achieved high levels of engagement and was distributed across multiple countries.

However, concerns later arose that certain materials closely resembled copyrighted works created by third parties.

Following claims brought by rights holders, some of the content was removed from online platforms, the company faced licensing and copyright demands, and legal proceedings were initiated in multiple jurisdictions.

The company was also exposed to contractual liability toward its own clients.

Key Risk: The issue was not the use of artificial intelligence to generate content. The problem was the failure to properly review and assess the content for intellectual property and copyright compliance before publication.

A rapidly growing company began using an AI-powered screening system to evaluate résumés because it received thousands of job applications each year.

The system automatically ranked candidates by analyzing their educational background, years of experience, industry expertise and historical recruitment data.

Initially, recruitment became significantly faster and human resources costs declined.
Over time, however, concerns emerged that applicants from certain age groups, graduates of particular universities and female candidates were consistently receiving lower scores.

Subsequent reviews revealed that the algorithm had learned from historical hiring data and had begun reproducing biases embedded in previous recruitment decisions.

The company faced allegations of discrimination, its recruitment practices became the subject of regulatory scrutiny and it suffered substantial reputational damage.

Key Risk: The issue was not the use of artificial intelligence in recruitment. The problem was the failure to regularly audit the algorithm’s outcomes and assess whether its decisions produced discriminatory effects.

A healthcare provider implemented an AI-assisted clinical decision support system to analyze medical imaging results. The system performed an initial assessment of radiological images and generated diagnostic recommendations for physicians.
In one case, a finding classified by the system as low risk was later diagnosed as a serious medical condition.

The patient subsequently initiated legal proceedings, alleging that the delayed diagnosis had caused significant harm.

The central legal question became:

• Who is legally responsible?
• The physician?
• The hospital?
• The software developer?
• The AI system provider?

Cases of this nature are expected to become one of the most significant areas of debate in healthcare law as artificial intelligence becomes increasingly integrated into clinical practice.

Key Risk: The issue was not the use of artificial intelligence to assist medical diagnosis. The problem was the failure to clearly define the limits of human oversight and the allocation of legal responsibility among the parties involved.

A technology company developed an AI-powered autonomous warehouse management system. The system was designed to manage the movement, classification and shipment of goods without direct human intervention.

Due to a software error, the system made incorrect operational decisions, resulting in substantial losses involving high-value products. The customer subsequently filed a claim for damages against the company.

At this stage, a critical legal question arose:

• Who is responsible for the loss?
• The software developer?
• The hardware manufacturer?
• The company operating the system?
• Or all parties jointly?

As the use of autonomous systems expands, disputes of this nature are expected to become one of the central issues in the legal regulation of artificial intelligence.

Key Risk: The issue was not the deployment of autonomous systems. The problem was the failure to define error scenarios, allocate legal responsibility and establish contractual risk-sharing mechanisms before the system entered operation.