When AI-generated code goes awry, the question of liability becomes a tangled web. Is it the product maker, the library coder, or the company using the AI-generated product? Our in-depth analysis explores this critical issue, especially in scenarios where catastrophic outcomes arise.
Ownership and Legal Implications of AI-Generated Code
In the first part of this series, we examined the ownership of AI-generated code and its legal implications. Now, let’s delve into the complexities of liability and exposure.
Functional Liability: Human vs. AI-Generated Code
Richard Santalesa, attorney and founding member of the SmartEdgeLaw Group, highlights that the legal implications of AI-generated code are not dissimilar to those of human-written code—for now. “Code, whether human- or AI-created, is rarely error-free,” says Santalesa. He emphasizes that software development often relies on third-party libraries and SDKs, which coders may not fully analyze before integration. This mirrors the current situation with AI-generated code.
Risks of Proprietary Code and Copyright Infringement
Sean O’Brien, a cybersecurity lecturer at Yale Law School, warns of a rising threat to developers: the inadvertent use of proprietary or copyrighted code generated by AI tools like ChatGPT or GitHub Copilot. These tools are trained on vast datasets, including proprietary and open-source code. Without transparency regarding training sources, developers face legal risks if AI-generated outputs unintentionally echo proprietary code.
O’Brien predicts a new wave of “trolling” akin to patent trolling, where entities target AI-generated works for alleged copyright violations. He warns, “As AI tools proliferate, software ecosystems could become riddled with proprietary code, leading to cease-and-desist claims.”
The Role of Biased or Flawed Training Data
Canadian attorney Robert Piasentin adds that the training data for AI tools may contain flawed, biased, or even copyrighted information. Outputs from these AI models could lead to claims if they result in damage or harm. Beyond legal trolls, there is the risk of malicious actors corrupting AI training datasets, further complicating the reliability of AI-generated code.
Determining Fault in Catastrophic Outcomes
When AI-generated code contributes to catastrophic failures, identifying responsibility is complex. Is it the company delivering the product, the library coder, or the firm that integrated the library into its system? The answer often involves all three.
Adding AI-generated code to the mix shifts most responsibility to the developer who chooses to use such code. Developers must rigorously test and validate AI-generated outputs to ensure functionality and compliance.
A Legal Frontier with Few Precedents
Currently, case law around AI-generated code is sparse. Until courts address these issues, developers, companies, and AI tool providers operate in a legal gray area.
Best Practices: Test, Test, and Test Again
In this uncharted legal and technical landscape, the safest course is meticulous testing. Ensure AI-generated code meets rigorous quality and compliance standards before deployment.
As the legal implications of AI tools evolve, staying informed and proactive is essential. This rapidly changing field requires vigilance from developers, businesses, and legal experts alike.
