In mid-2024, a senior machine learning engineer at a major AI company discovered that the company's flagship model had a systematic tendency to produce dangerously inaccurate outputs in medical contexts. The engineer documented the problem, reported it through internal channels, and recommended that the company restrict the model's use in healthcare applications until the issue was resolved. Management acknowledged the report. Then they did nothing. The model continued to be marketed to healthcare clients. The engineer was removed from the project and, within three months, was terminated for what the company described as "performance reasons."

This engineer is now a plaintiff in a whistleblower retaliation lawsuit. And this case is not unique. I am aware of multiple similar matters, either filed or in pre-litigation, involving AI engineers who raised internal concerns about defective or dangerous AI systems and faced professional retaliation for doing so. We are at the beginning of what I expect to become a significant wave of AI whistleblower litigation.

Why AI Whistleblowing Is Different

Whistleblowing in the technology industry is not new. But AI whistleblowing presents unique characteristics that distinguish it from prior waves of tech industry whistleblower activity.

The knowledge is concentrated. In traditional software engineering, many developers can identify bugs and security vulnerabilities. In AI development, the knowledge required to identify a model's failure modes is highly specialized. The engineers who train, evaluate, and red-team AI models possess technical knowledge that is often inaccessible to management, regulators, and the public. When these engineers identify a serious problem, they may be the only people in the organization who fully understand its implications. This concentration of knowledge creates both a heightened obligation to speak and a heightened vulnerability when they do.

The harms are probabilistic. A traditional product defect is deterministic: the brake fails, the bridge collapses, the drug causes a side effect. AI defects are probabilistic: the model produces dangerous outputs some percentage of the time, for some subset of inputs, in ways that may not manifest until the system is deployed at scale. This makes it easier for management to dismiss internal warnings as overly cautious or speculative. "The model works fine 98 percent of the time" is a response that sounds reasonable in a boardroom but is catastrophic in a hospital.

The commercial pressure is extraordinary. AI companies are operating in an environment of intense competitive pressure and enormous valuations. The incentive to ship products quickly, to claim capabilities that have not been fully validated, and to minimize the significance of known limitations is immense. Engineers who raise concerns about safety or reliability are, in effect, threatening the company's market position and valuation. This creates a structural conflict between engineering integrity and commercial interest that is more acute than in most other industries.

The engineers who build these systems understand their limitations better than anyone. When those engineers are silenced, the public loses its most important source of information about AI risk.

The Legal Theories

AI whistleblower cases can involve multiple overlapping legal theories, and the technical evidence required varies by claim.

Products liability. When an AI engineer identifies a defect in a deployed product and the company fails to address it, the engineer's internal reports become critical evidence in subsequent products liability litigation. The engineer may serve as a fact witness or, in some cases, as a plaintiff in a separate retaliation claim. From my perspective as a technical expert, the engineer's internal documentation of the defect, including test results, failure analyses, and recommended fixes, is often the most compelling evidence in the products liability case. It demonstrates that the company had actual knowledge of the defect and chose not to act.

Securities fraud. For publicly traded AI companies, the gap between what the company tells investors about its AI capabilities and what engineers know internally about the system's limitations can constitute securities fraud. If the company represents to investors that its AI system is accurate, reliable, and safe, while engineers internally are documenting systematic failures, that creates potential liability under Section 10(b) of the Securities Exchange Act and Rule 10b-5. The engineer's whistleblower complaint may trigger SEC investigation, and the Dodd-Frank Act provides significant financial incentives and anti-retaliation protections for securities whistleblowers.

Regulatory violations. In regulated industries such as healthcare, financial services, and transportation, deploying AI systems with known defects may violate industry-specific regulations. An engineer who reports these violations to regulators is protected under various sector-specific whistleblower statutes. The FDA, for example, has increasing oversight of AI-enabled medical devices, and an engineer who reports that a deployed medical AI has known safety issues may be protected under federal whistleblower protections.

Retaliation Claims

The retaliation claim is often the core of the whistleblower's personal lawsuit. To prevail, the engineer must show that they engaged in protected activity (reporting a genuine concern about illegality, safety, or fraud), that they suffered an adverse employment action (termination, demotion, reassignment), and that there is a causal connection between the two.

AI companies have developed sophisticated methods of retaliating against internal dissenters without creating an obvious paper trail. Common patterns include reassignment to low-impact projects, exclusion from key meetings and decision-making, negative performance reviews that begin shortly after the internal complaint, and organizational restructurings that conveniently eliminate the whistleblower's position. Technical expert analysis can help establish these patterns by examining the timeline and context of employment decisions relative to the whistleblower's protected activity.

The Evidence Challenge

AI whistleblower cases present unique evidentiary challenges that require technical expertise to navigate.

Proving the defect exists. The whistleblower claims the AI system is broken. The company claims it works as intended. Resolving this dispute requires detailed technical analysis of the model's architecture, training data, evaluation results, and deployment performance. As an expert, I can examine the model's documented failure modes, reproduce the failures the whistleblower identified, and assess whether the company's response to the internal report was technically adequate.

Establishing materiality. For securities fraud claims, the whistleblower must show that the concealed information was material, meaning it would have been important to a reasonable investor. Proving that an AI defect is material requires translating technical risk into business impact: potential liability exposure, regulatory risk, reputational harm, and the probability that the defect will manifest in ways that affect the company's financial position. This is inherently a technical assessment.

Preserving technical evidence. AI models are continuously updated, retrained, and modified. The version of the model that exhibited the defect the whistleblower reported may no longer exist by the time litigation begins. Early preservation requests targeting model checkpoints, training data, evaluation logs, and deployment configurations are essential. Without these artifacts, it may be impossible to reconstruct the technical basis of the whistleblower's complaint.

What Comes Next

The AI whistleblower wave is still in its early stages. Several high-profile departures from major AI companies in 2024 and 2025 have been accompanied by public statements expressing concern about safety and responsibility. Not all of these will result in litigation, but some will. And as AI systems are deployed in increasingly high-stakes domains, the consequences of ignoring internal safety concerns will become more severe, the legal exposure for companies that suppress dissent will grow, and the incentive for engineers to pursue legal remedies will increase.

For attorneys representing AI whistleblowers, the technical dimension of these cases is not optional. The whistleblower's credibility depends on demonstrating that their technical concerns were legitimate, that the defect was real, and that the company's failure to act was unreasonable. Expert witness testimony that validates the whistleblower's technical analysis and contextualizes it within industry standards is essential to establishing these elements.

The engineers who build AI systems are, for now, the most important source of truth about what those systems can and cannot do. Protecting their ability to speak honestly about the technology they create is not just a matter of employment law. It is a matter of public safety.

The Criterion AI provides expert witness services and litigation support for matters involving artificial intelligence, machine learning, and algorithmic decision-making. For a confidential consultation on an active or anticipated matter, contact us at criterion@thecriterionai.com or call (617) 798-9715.