Intelligent CIO Q&A with Nerijus Šveistys, Senior Legal Counsel, Oxylabs, on rapid legal developments around GenAI and how businesses can adapt.
The AI boom was followed by the rush to regulate commercial AI development. What are the main legal outcomes of this rush so far?
The boom of the last few years appears to have sparked a push to establish regulatory frameworks for AI governance. This is a natural development, as the rise of AI seems to pose issues in data privacy and protection, bias and discrimination, safety, intellectual property and other legal areas – as well as ethics that need to be addressed.
As for specific outcomes, China already started regulating the use of certain AI models back in 2021. We also have the AI Act of the EU which just came into force and will become fully effective by 2026. Other jurisdictions are following up with their own measures regulating the AI ecosystem.
What are the differences between the approach to AI regulation in the EU and other jurisdictions?
The main difference we can see is the comparative quickness with which the EU has released a uniform regulation to govern the use of all types of AI. Other large jurisdictions, such as China and the US, appear to have taken different approaches. China is regulating specific areas of AI step-by-step, addressing what is recognized as risks. In 2021, they introduced regulation on recommendation algorithms, which have by then increased their capabilities in digital advertising. It was followed by regulations on deep synthesis models or, in common terms, deepfakes and content generation in 2022. And then, in 2023, regulation on generative AI models was introduced as these models were making a splash in commercial usage.
Meanwhile, the US has not yet enacted any federal-level AI regulations. There are proposed regulations at the state level, such as the so-called California AI Act – but even if they come into power, it may still take some time before they do.
Is there a delay in regulation due to the pushback from business? Can these differences in regulation lead to unequal business development and innovation?
They certainly can. More rigid regulatory frameworks may impose compliance costs for businesses in the AI field and stifle competitiveness and innovation. On the other hand, they bring the benefits of protecting consumers and adhering to certain ethical norms.
As for the pushback, there was pushback to the EU AI Act, too, which was nevertheless introduced. Thus, it is not clear whether the delay in the US is only due to lobbyism or other obstacles in the legislation enactment process. It might also be because some still see AI as a futuristic concern, not fully appreciating the extent to which it is already a legal issue of today.
Generally, what falls under AI regulation? Is it only the laws that specifically target AI development, or does it also include related laws, such as those governing data collection?
AI regulations do not only target AI development, as they may cover several issues such as data privacy and protection, intellectual property, consumer protection, AI deployment and use, liabilities for not following the regulations and so on. For example, the EU AI Act also covers the usage of AI in physical devices, such as elevators.
Additionally, all businesses that collect data for advertisement are potentially affected as AI regulation can also cover algorithmic bias in targeted advertising.
What is the impact of AI regulation on closely related industries like web scraping?
AI has significantly impacted the web scraping industry and is likely to continue to do so. From data collection, validation, analysis or overcoming anti-scraping measures, there is a lot of potential for AI to massively improve the efficiency, accuracy and adaptability of web scraping operations. While it is too soon to say now, any regulation on AI may accordingly impact the above-mentioned web scraping areas that involve AI as well.
AI regulations may also bring the spotlight on certain areas of law that were always very relevant to the web scraping industry, such as privacy or copyright laws. At the end of the day, scraping content protected by such laws without proper authorization could always lead to legal issues, and now so can using AI this way.
Over the last few years, many cases involving AI have been widely covered in the media. Which would you say are potentially the most impactful?
Perhaps the most widely covered cases involving AI have been the lawsuits against GenAI models. Owners of such models, like OpenAI and its main investor, Microsoft, were sued by numerous authors, artists and musicians, alleging that their copyrights were infringed via the models being trained using their original works without permission. These cases are pivotal in determining the legal boundaries of using copyrighted material for AI development and establishing legal precedents for protecting intellectual property in the digital age.
What can businesses do now to prepare for the future – given that such cases can take years to come to a conclusion and become a precedent?
Speaking about the specific cases of using copyrighted material for AI training, businesses should approach this the same way as any web-scraping activity – that is, evaluate the specific data they wish to collect with the help of a legal expert in the field.
As for AI governance in general, it is important to recognize that the AI legal landscape is very new and rapidly evolving, with not many precedents in place to refer to as of yet. Hence, continuous monitoring and adaptation of your AI usage are crucial.
