How the rise of AI-related legal challenges reshapes the future of technology disputes.
Is arbitration losing ground?
In 2020, my article in the Contracting Excellence Journal (CEJ)1 captured an emerging reality: technology-related disputes were steadily migrating from traditional courtrooms to specialized arbitration forums. At that time, the exodus was powered by a combination of expertise, confidentiality, and efficiency – a winning formula for litigating the complex, fast-evolving realm of technology contracts. The intricate code, specialized industry jargon, and rapidly shifting landscapes made arbitration a natural choice to resolve these disputes.
Fast forward to 2025 – while the landscape has evolved dramatically, the evolution itself is exciting. The infusion of Artificial Intelligence (AI) and Large Language Models (LLMs)2 into technology contracts, from software licensing and cloud computing arrangements to AI-as-a-Service models,3 has introduced new complexities. These very complexities are now fueling a resurgence of litigation, raising important questions about whether traditional arbitration can continue to serve as the optimal solution for these modern challenges.
Several trends are contributing to these complexities.
The AI-driven transformation: data and case insights
AI has become a critical factor in driving legal disputes across multiple sectors. Recent statistics indicate a marked increase in cases where AI is central. For example, U.S. courts reported a series of high profile copyright disputes by late 2024 involving AI training practices. One high-profile case saw a major news organization sue an AI developer for allegedly using its copyrighted articles without permission to train their Large Language Model (LLM). This case underscores the challenge of applying traditional copyright laws to AI-driven processes and highlights questions about authorship and fair use.4
Algorithmic bias and public scrutiny
Beyond intellectual property, algorithmic bias is emerging as a significant trigger for litigation. Consider an AI-powered hiring tool that systematically disadvantages female candidates. Such issues transcend technical glitches, they strike at the heart of public accountability and ethical practice. When disputes involve systemic bias, the confidentiality of arbitration may hinder broader societal debate and the development of legal precedents. Public litigation, with its transparency and potential for judicial oversight, offers a path for comprehensive redress.
Securities litigation and “AI washing”
The financial sector is also feeling the impact. The phenomenon of “AI washing”, where companies exaggerate their AI capabilities to attract investors, has led to a surge in securities class action lawsuits. One notable case involved a tech firm accused of overstating its AI integration, resulting in inflated stock valuations and significant investor losses. This trend underscores the importance of transparency and accountability, which litigation is better equipped to provide.
The double-edged sword of arbitration in the AI era
Arbitration has long been valued for its efficiency and expertise. Yet, the “black box” nature of many AI systems poses a significant challenge. When AI decision-making is opaque, parties cannot fully understand or contest the rationale behind a decision. For example, if an AI algorithm denies a loan without clear reasons, the lack of transparency can erode confidence in the arbitration process. This problem is prompting legal professionals to question whether arbitration is always the best forum for disputes involving AI-driven processes.
Another limitation of arbitration is its inherent privacy. While confidentiality is beneficial, it means that arbitration awards do not contribute to the public body of law. In rapidly evolving areas such as AI ethics and liability, the absence of published decisions limits the development of legal standards. Litigation, by contrast, produces precedents that help shape future legal interpretations and policy.
In response to these challenges, the legal community is beginning to harness AI to enhance dispute resolution itself. The concept of “explainable AI” is gaining traction. These systems aim to clarify the factors influencing AI decisions, thereby improving transparency and accountability in arbitration. Early adopters report that explainable AI tools can reduce costs and resolution times while building trust among disputing parties.
Hybrid models as the way forward: combining the best of both worlds
Recognizing both the strengths and limitations of arbitration, some institutions are exploring hybrid dispute resolution models. Under these frameworks, disputes may initially be addressed through arbitration to benefit from its speed and technical focus. If broader issues with public interest or precedent arise, parties can escalate the matter to litigation. Such a model preserves confidentiality for technical disputes while ensuring that significant legal questions are resolved publicly, contributing to a more robust legal framework.
Strategic recommendations for legal practitioners and corporate counsel
For legal professionals, corporate counsel, and in-house advisors, adapting to this evolving dispute resolution landscape is critical. Here are key recommendations:
- Adopt a hybrid dispute resolution strategy: Structure contracts with provisions that allow for initial arbitration, with the option to escalate to litigation when public accountability or precedent is necessary.
- Invest in AI literacy: Enhance your understanding of AI technologies and their legal implications through continuous education, industry conferences, and guided use of AI tools, as available.
- Strengthen contractual provisions: Ensure arbitration clauses account for AI complexities by incorporating clear guidelines for transparency and addressing algorithmic decision-making.
- Collaborate with industry peers and regulators: Engage in forums and working groups focused on AI and dispute resolution to help develop best practices and inform regulatory standards.
- Monitor regulatory developments: Stay updated on regulatory changes affecting arbitration clauses, particularly those involving consumer rights and sensitive data.
Regulatory insights and their impact
Regulatory bodies are playing an increasingly active role in shaping the future of dispute resolution in the AI era. The U.S. Federal Trade Commission (FTC), for instance, has scrutinized arbitration clauses that may undermine consumer protections. In a notable case involving IXL Learning, the FTC supported a challenge to an arbitration clause linked to unauthorized data collection from children, emphasizing that dispute resolution mechanisms must not infringe on statutory rights. Such regulatory interventions compel legal professionals to align contractual terms with current legal standards, thereby safeguarding both client interests and broader public policy.
Future outlook: a new era of legal complexity and opportunity
The integration of AI into technology contracts will undoubtedly accelerate, intensifying both innovation and complexity in dispute resolution. Challenges such as algorithmic opacity (lack of transparency) and ethical considerations are likely to persist, but they also offer opportunities for those ready to innovate. The future lies not in choosing between arbitration and litigation, but in intelligently integrating both. Hybrid models—combining arbitration’s efficiency with litigation’s transparency—could form the backbone of a more dynamic and responsive legal system.
As legal frameworks continue to evolve and new technologies emerge, continuous dialogue among legal professionals, technologists, and regulators will be essential. Such collaboration will help create a legal system that is both adaptive and resilient, capable of meeting the challenges of an AI-driven world.
Conclusion: embracing a future of innovation and accountability
The resurgence of litigation in AI and technology contracts should be seen not as a failure of arbitration, but as a call to innovate. The legal and outsourcing communities stand at a pivotal juncture, where adapting dispute resolution strategies to address the nuances of AI is imperative. By embracing a hybrid approach that leverages the strengths of both arbitration and litigation, we can achieve outcomes that are efficient, transparent, and just.
For legal professionals, corporate counsel, and policymakers, this integrated model represents a roadmap for navigating the complexities of modern technology disputes. It is an invitation to reimagine traditional methods, foster continuous learning, and ultimately, build a legal framework that is as forward-thinking as the technologies it governs.
In an era defined by rapid technological change, the interplay between AI, outsourcing, and dispute resolution offers both challenges and remarkable opportunities. With strategic adaptation and a commitment to innovation, the future of dispute resolution promises to be as dynamic and multifaceted as the world it seeks to serve.
REFERENCES:
General shift from arbitration to litigation
1. Strong, S. I. (2016). The Impact of Technology on International Arbitration and Litigation: Courts, Arbitration, and the Future of Dispute Resolution. Missouri Journal of Dispute Resolution.
2. Drahozal, C. R., & Friel, S. (2020). Technology Arbitration: How It Works and Why It’s Changing. Journal of Dispute Resolution.
AI-related disputes in court
3. Hartzog, W. (2020). Privacy’s Blueprint: The Battle to Control the Design of New Technologies. Harvard University Press.
4. Citron, D. K. (2022). The Fight for Privacy: Protecting Dignity, Identity, and Love in the Digital Age. W. W. Norton & Company.
5. Reese, R. A. (2023). Artificial Intelligence and Copyright Law: Emerging Trends. Stanford Law Review.
Algorithmic bias and AI liability
6. Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.
7. Pasquale, F. (2020). New Laws of Robotics: Defending Human Expertise in the Age of AI. Harvard University Press.
Intellectual property battles in AI
8. Guadamuz, A. (2023). Artificial Intelligence and Copyright: Ownership, Fair Use, and the Public Domain. Journal of Intellectual Property Law.
9. Samuelson, P. (2022). The Copyright Implications of AI-Generated Works. Berkeley Technology Law Journal.
AI washing and securities litigation
10. U.S. Securities and Exchange Commission (2024). SEC Enforcement Actions Against AI Misrepresentation in Public Filings.
11. Coffee, J. C. (2023). Securities Litigation and Market Fraud in the AI Era. Columbia Law Review.
ABOUT THE AUTHOR
Irina Beschieriu is a tech transactions, outsourcing and commercial contracts attorney based in Dallas, Texas. She currently serves as Deals Counsel for Atos IT Solutions and Services, Inc., part of the Atos Group, where she focuses on complex technology and outsourcing agreements. An international counsel, with an LLM Degree in Advocacy and Dispute Resolution from Benjamin Cardozo School of Law, New York and a New York-barred attorney, she holds the WCC Certified Contract Management Advanced Practitioner (CCMAP) and CIPP/EU certifications, with expertise in contract negotiation, dispute resolution, contract lifecycle management, data privacy, and cross-border compliance. Passionate about the intersection of law and emerging technologies, she advocates for AI literacy in legal practice and the modernization of contract management.
ABOUT ATOS
Atos IT Solutions and Services, Inc. is a global leader in digital transformation, providing cybersecurity, cloud, and high-performance computing solutions to enterprises and public sector organizations worldwide.
END NOTES
1. Is it a trend? Are technology-related disputes removed from courts and sent to specialized dispute resolution forums? Contracting Excellence Journal (CEJ)
2. LLM defined: “Artificial Intelligence (AI) is a broad field of computer science focused on creating intelligent machines that can mimic human capabilities. A Large Language Model (LLM) is a specific type of AI specifically designed to understand and generate human language, allowing it to perform tasks like text generation, translation, and answering questions, by learning patterns from vast amounts of text data; essentially, an LLM is a powerful tool within the larger AI spectrum focused on natural language processing. (Source: AI Overview online)
3. AI-as-a-Service (AIaaS) models are services that allow businesses to use artificial intelligence (AI) without having to build their own AI systems. (Source: AI Overview online)
4. News article, AI Infringement Case Updates: January 13, 2025 Also, "LLM" stands for "Large Language Model," which refers to a type of artificial intelligence system trained on massive amounts of text data, allowing it to understand and generate human-like text, perform tasks like translation, summarization, and answer questions in a comprehensive manner, all based on the information it has learned from the data it was trained on. (Source of LLM definition: AI Overview online).
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