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EU AI Regulations: AI Disputes and Litigation

Writer's picture: Rita ShethRita Sheth

Updated: May 27, 2024

The European Union's regulatory framework for artificial intelligence (AI) marks a significant step in addressing the complexities introduced by AI technologies in various sectors. As these regulations impose stringent compliance standards, they pave the way for a range of AI legal disputes and litigations, including within the realms of compliance, intellectual property, liability, contractual obligations, and cross-border enforcement. Understanding these emerging legal challenges is crucial for stakeholders, ranging from developers and users of AI technologies to legal professionals navigating this evolving landscape.


1. Compliance Disputes


At the core of the EU's AI strategy are the regulations outlined in the AI Act, which classify AI systems according to their risk levels, imposing rigorous duties on high-risk applications. These include obligations related to transparency, data governance, and accountability.


AI disputes may arise when there is an alleged failure by AI developers or deployers to adhere to these stipulated standards. Such litigation often focuses on the implementation of data protection measures, the fairness and accuracy of algorithmic decisions, and the level of transparency maintained in AI operations.


For example, consider a scenario where a healthcare provider employs an AI system for patient diagnosis that is later found to exhibit bias based on gender or ethnicity, leading to substandard care for certain groups. This could trigger litigation centred around non-compliance with the EU's stringent requirements for fairness and transparency in high-risk AI applications.


2. Intellectual Property & JV Conflicts


AI's ability to generate content autonomously challenges the traditional boundaries of intellectual property law. The EU's regulations attempt to address issues of authorship and ownership of AI-generated content, which remain hotly contested in legal arenas. Disputes often involve determining the rights to outputs created by AI, particularly when such outputs involve significant creative or commercial value. This has already come up in NFT related disputes and likely will also be explored in the context of AI generated content and lead to AI legal disputes.


JV disputes may also arise, for example, between an AI development company and a client over the ownership of machine-learning models. The legal contention might focus on whether the IP rights belong to the company that provided the initial dataset or the developers who refined and trained the models. The complex way in which models are developed and the increasing use of outsourced technology development also makes this kind of dispute or challenge more likely.


3. Liability and Damage Claims


Liability in AI-related incidents is a critical area of litigation, especially concerning damages or injuries caused by autonomous systems, such as drones or self-driving vehicles. The EU is considering adjustments to liability frameworks to ensure victims can seek redress effectively. These disputes typically revolve around assigning responsibility, whether to manufacturers, software developers, or users.


For example, in the event of a self-driving car accident, determining liability involves assessing whether the cause was a flaw in the AI's decision-making software, a manufacturing defect, or user misuse. The complexity of these cases lies in dissecting the layered interactions between software programming, hardware stability, and human oversight. Another interesting areas where there may well be AI related disputes is in the arena of AI investment managers or traders where failures could result in significant financial loss or breaches of FS regulatory regimes.


4. Contractual Disagreements


AI's integration into business operations introduces complexities in contractual fulfillments, particularly in sectors reliant on AI for decision-making, like finance or logistics. Disputes may emerge over AI's adherence to performance metrics stipulated in contracts, or its role in significant business losses due to errors or inefficiencies.


For example, a logistics company might face a breach of contract claim if their AI-driven routing system fails to optimise delivery routes, resulting in substantial delays and financial losses. These cases test the contractual boundaries of performance guarantees and the interpretative flexibility of legal terms under unforeseen technological contexts.


5. Cross-Border Enforcement Challenges


The global nature of AI technology complicates the enforcement of EU regulations, especially when non-EU entities interact with EU consumers or process their data. Legal disputes in this area involve complex jurisdictional issues and the applicability of EU law to entities outside its borders. For example, a U.S based AI firm, providing services to EU customers without proper adherence to the EU's AI regulations, could be subject to litigation that tests the reach of EU law and international cooperation in enforcement.


Conclusion


The above areas are just the tip of the iceberg. There are likely to be a vast range of areas that give rise of new types of disputes, as we continue to navigate the challenges thrown up by this emerging technology.


The diverse array of disputes and litigations arising from these regulations underscores the need for continued legal innovation and adaptation. For businesses staying ahead means not only understanding regulations but actively engaging in shaping the policies that govern AI's future and and evaluating risks inherent in new products to ensure they mitigate AI litigation risk.

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