The integration of large language models (LLMs) like GPT-based systems into app-based platforms is accelerating. But as LLMs shift from back-end analytics to front-facing, decision-making components of gig apps, they also inherit a new set of risks—particularly product liability exposure traditionally reserved for physical products or design flaws in software systems.
The Expanding Definition of a “Product”
Courts and regulators are beginning to broaden the definition of “product” to cover digital goods and AI systems. (See my previous blog on this topic HERE).
“Although historically courts have been hesitant to apply product liability principles to websites and other software products that provide matching services to users (such as social media and dating websites), there is a recent trend of expanding product liability theories towards these platforms.”
Doe v. Lyft, Inc., 756 F. Supp. 3d 1110, 1119 (D. Kan. 2024). In Doe, the Court held that the Lyft app could be considered a product under product liability law because of its similarities to a tangible product, but plaintiffs must demonstrate a specific defect in the app that caused the injury.
Traditionally, code alone wasn’t considered a “product” under strict liability laws. However, when AI software is embedded into a physical device or commercial platform, or when it makes autonomous decisions affecting users’ safety or financial security, the risk assessment shifts. As apps incorporate LLMs, they are increasingly seen as defective products rather than just services.
Potential Product Liability Theories
LLM-based apps in the gig economy could face claims under several familiar theories:
- Design Defect – if the AI system was designed without sufficient guardrails or human override mechanisms.
- Failure to Warn – if users weren’t adequately warned about the system’s limitations or potential for error.
- Manufacturing Defect (Data Defect) – if training data was corrupted, biased, or incomplete in a way that causes harm.
Developers should anticipate that plaintiffs’ counsel will argue that “predictive errors” or “hallucinations” in AI outputs are similar to defective instructions in traditional product law.
Practical Design Tips to Reduce Liability Exposure
Maintain Human-in-the-Loop Control
Even when AI automates decisions, ensure that final or consequential outputs (e.g., driver suspensions, route changes, payment calculations) are subject to human review or approval.
Courts view human oversight as strong evidence of reasonable care and a break in cau
Embed Contextual Disclaimers and Use Warnings
Integrate “just-in-time” disclaimers directly in user interfaces—especially for advice-generating or decision-support features. These should:
- Clarify that outputs are generated by AI and might not be accurate.
- Discourage unsafe reliance (e.g., navigation, medical, or legal advice contexts).
- Be conspicuously presented at the moment of use, not buried in Terms of Service.
Document the Model Lifecycle
Keep detailed records of:
- training datasets and updates
- model fine-tuning decisions
- testing logs for bias, safety, and error rates
Discovery requests in AI-related product cases increasingly target traceability of model behavior. Good documentation can demonstrate due care and cut off punitive exposure.
Separate the “Service” from the “Product”
Architect the system so that the LLM’s outputs are clearly part of a service interaction rather than a discrete “product” sold to consumers. For example:
- Host AI processing on your servers (SaaS model).
- Frame responses as informational or advisory, not directive.
- Avoid marketing language suggesting reliability or precision guarantees.
Build a Continuous Monitoring & Recall Protocol
Establish internal policies for “AI recalls”—the digital equivalent of product recalls. When bugs, bias, or unsafe behaviors emerge, have a protocol for suspending model outputs, notifying users, and updating training data.
This type of safety program demonstrates proactive management and can be a strong affirmative defense in negligence and strict liability claims.
Looking Ahead: Regulating AI Products
Regulators are beginning to treat AI systems as products subject to safety and labeling standards, including the EU AI Act and various U.S. state bills addressing “automated decision systems.” Developers in the gig-economy sector should expect heightened scrutiny and potential duties to audit and disclose model performance.
LLMs are redefining what “defect,” “foreseeability,” and “warning” mean in the digital age. Developers who think like product manufacturers—testing, documenting, and warning—will be the ones best positioned to innovate safely and avoid litigation.
Bottom Line
AI development in the gig economy is not just about innovation—it’s about defensible design.
Treat every AI decision node as a potential deposition exhibit. Build transparency, traceability, and human control into your systems today, and you’ll stay ahead of tomorrow’s liability landscape.
About Christian & Small
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