Practical Tips for Avoiding Legal Issues in AI Applications
Artificial Intelligence (AI) is the buzzword of the moment, and it's not hard to see why. The potential applications are vast and exciting, but with this new frontier comes a host of legal considerations. As AI continues to evolve, so too does the legal landscape surrounding it. While there isn't a lot of established law in this area yet, we can make some educated guesses about potential legal pitfalls and how to avoid them.
Understanding Inputs and Outputs
When it comes to generative AI, the key legal considerations revolve around the inputs and outputs of the AI system. The prevailing view among AI companies is that if their training data includes protected intellectual property, the training itself is considered fair use and doesn't infringe on the IP owner's rights. This stance is often reinforced by using training data and models created by academic institutions or nonprofits.
From a privacy and data protection perspective, the trigger for data input is whether it's personally identifiable information (PII). If PII is involved, the usual rules around consumer transparency, purpose use limitations, and establishing a valid legal basis apply. These can be particularly tricky with AI models where the range of end use cases is unknown. As a result, most companies opt to train on anonymized data sets.
The question of whether an AI's output can infringe is less clear-cut. Experts are less comfortable about the defensibility of a case where most or all of the inputs used to generate an outcome belong to one rights holder. For example, feeding a model with 15 Kurt Vonnegut books and asking it to generate a novel could potentially infringe on copyright laws. This area remains largely untested, but it's worth keeping an eye on as the law evolves.
Algorithmic Decision-Making and Partnerships
Algorithmic decision-making is a slightly more established topic, thanks to the visibility of large tech platforms and the rise of alternate creditworthiness projects. Legal guidance in this area generally requires transparency around how algorithms process personal information and warns against algorithmic decision-making that has a disparate impact on people based on protected characteristics.
When outsourcing AI technology, the vendor or partner who operates the AI system is typically in a better position to understand the risks associated with inputs and outputs. These third parties usually manage their own legal risk through limitations of liability and disclaimers in their standard terms. However, it's a good idea to negotiate terms that allocate the risk appropriately to that third party, especially when AI plays a critical role in a product offering.
Competition and Other Legal Areas
There have been some explorations into whether AI can facilitate collusion among competitors that violates competition laws. There's also a theoretical possibility that multiple AI systems, acting independently, could become so effective at processing market data or predicting trends that their output is very similar to actual collusion.
Other areas of law that could touch on AI include products liability claims related to AI-powered wearables, AI in the employment context, or the use of AI in litigation and legal discovery.
In conclusion, while the legal landscape surrounding AI is still developing, it's crucial to stay informed and proactive in addressing potential legal issues. As always, if you have any questions or concerns, don't hesitate to reach out to your legal team for guidance.