Artificial Intelligence Litigation and the Critical Role of Expert Witnesses

Artificial intelligence (AI) and machine learning have rapidly transformed how businesses, consumers, and institutions interact with technology. What was once limited to recommendation engines and automated workflows now includes generative AI systems capable of producing text, images, software code, medical analyses, and business decisions with minimal human input. As AI systems become embedded in healthcare, finance, transportation, manufacturing, and consumer products, they are creating new efficiencies—but also new avenues for litigation.

Here, we examine how artificial intelligence is reshaping civil claims, the legal challenges courts increasingly face, and the role expert witnesses play in helping judges and juries understand highly technical AI systems.

What Is Artificial Intelligence?

Artificial intelligence refers to computer systems designed to perform tasks that typically require human cognition, such as learning, reasoning, language processing, and decision-making. Modern AI systems analyze large datasets, identify patterns, and adapt their outputs based on new information or user interaction.

While AI research has existed for decades, recent advances in computing power and data availability have accelerated its use across nearly every industry. Today, AI powers technologies ranging from search engines and recommendation platforms to autonomous vehicles, medical imaging systems, fraud detection tools, and generative AI applications capable of producing human-like text, images, and code.

As AI systems become more integrated into business and consumer products, they are also raising increasingly complex legal questions involving liability, transparency, privacy, intellectual property, and negligence.

Major Issues in Litigation Involving Artificial Intelligence

As AI systems become more autonomous, litigation involving these technologies has grown substantially more complex. Unlike traditional software, many AI systems evolve through training data and continuous learning processes rather than through static, human-written instructions alone. This can make identifying the source of an error particularly difficult.

In conventional software disputes, investigators may trace a malfunction to a specific coding error or design defect. AI-related claims, however, often involve probabilistic decision-making, opaque model architecture, or training data issues that are not immediately apparent—even to the developers themselves. Courts are increasingly confronting what many researchers refer to as the “black box” problem: highly sophisticated AI systems capable of generating outputs without providing transparent explanations for how those outputs were reached.

This lack of transparency has implications across multiple areas of civil litigation, including:

• Product liability claims involving autonomous or semi-autonomous systems
• Healthcare malpractice disputes involving AI-assisted diagnoses
• Employment litigation tied to algorithmic hiring or workplace monitoring tools
• Intellectual property disputes concerning AI-generated content
• Consumer protection and privacy claims involving biometric or predictive analytics systems

Questions surrounding bias and discrimination have also become central to AI litigation. Several lawsuits and regulatory investigations have focused on allegations that AI systems produced discriminatory outcomes in hiring, lending, insurance underwriting, housing, and criminal justice applications. In many of these cases, plaintiffs argue that biased training data or flawed model development resulted in disparate impacts on protected groups.

Regulators are also paying closer attention to AI-related risks. The European Union’s AI Act, emerging federal guidance in the United States, and state-level legislation governing automated decision-making systems are beginning to establish compliance obligations for companies deploying AI technologies. As regulatory frameworks evolve, civil litigation is likely to expand alongside them.

Challenges of Proving Liability in AI Cases

Determining liability in AI litigation often requires untangling a complicated web of developers, software vendors, hardware manufacturers, data providers, and end users. In some cases, multiple entities may contribute to the operation of a single AI system.

For example, an autonomous vehicle accident could involve allegations against:

• The vehicle manufacturer
• The developer of the driving algorithm
• Third-party sensor manufacturers
• Mapping or navigation data providers
• Fleet operators or maintenance companies

Similarly, an AI-assisted medical error may raise questions regarding physician oversight, hospital implementation practices, software reliability, and regulatory compliance.

Causation can become especially difficult when AI systems continuously update or refine their outputs over time. Plaintiffs and defendants alike may require extensive forensic analysis to determine whether a particular outcome resulted from flawed training data, improper implementation, inadequate human oversight, cybersecurity vulnerabilities, or unforeseeable system behavior.

These technical questions often exceed the knowledge of judges and juries, making expert testimony particularly important.

The Role of Expert Witnesses in Artificial Intelligence Litigation

Expert witnesses frequently play a central role in helping courts understand how AI systems function and whether a system operated as intended. Because artificial intelligence encompasses a broad range of technologies, the type of expert retained will depend heavily on the facts of the case.

AI-related litigation may require testimony from experts in:

• Computer science and software engineering
• Machine learning and neural network architecture
• Robotics and autonomous systems
• Data science and statistical modeling
• Cybersecurity and digital forensics
• Human factors engineering
• Healthcare informatics
• Regulatory compliance and AI governance

These experts may be asked to evaluate training data, audit model outputs, reconstruct system failures, assess algorithmic bias, or explain technical concepts to juries in understandable terms.

Importantly, modern AI litigation increasingly demands experts who can address not only technical performance but also governance and risk-management practices. Courts and regulators are scrutinizing whether companies adequately tested AI systems, monitored for foreseeable harms, documented decision-making processes, and implemented meaningful human oversight.

The ability to explain complex AI concepts clearly has become essential. Jurors may struggle to understand how a machine-learning model reaches conclusions, particularly when those conclusions involve probabilities rather than deterministic outcomes. Experts capable of translating technical processes into accessible testimony can significantly influence how fact-finders evaluate liability and damages.

The Expanding Future of AI Litigation

As artificial intelligence becomes more deeply integrated into commercial and consumer environments, litigation involving these technologies will likely continue to expand. Emerging disputes may center on issues such as deepfakes, synthetic media, autonomous decision-making, AI-generated misinformation, copyright infringement, and cybersecurity vulnerabilities created by generative AI systems.

At the same time, AI is beginning to reshape the legal profession itself. Law firms and litigation support providers increasingly use AI-powered tools for document review, legal research, predictive analytics, and e-discovery. Courts are also evaluating ethical and procedural questions surrounding attorneys’ use of generative AI in legal filings and case preparation.

While artificial intelligence introduces substantial efficiencies, it also creates novel legal risks that courts, regulators, and litigants are only beginning to address. As these technologies continue to evolve, expert witnesses will remain critical in helping legal professionals navigate the technical, ethical, and regulatory complexities at the center of AI-related disputes.