The Generative AI (gen AI) boom of the last few years 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.
Highlights of specific outcomes include China 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.
AI regulation in the EU vs other jurisdictions
The comparative quickness with which the EU has released a uniform regulation to govern the use of all types of AI makes it stand out. 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.
The US has not yet enacted any federal-level AI regulations. There are quickly advancing regulations at the state level, such as the California AI Act. However, it may still take some time before they take effect, even after going through all the legislative hurdles. Is regulation halting innovation?
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.
Businesses are pushing back, and there is still plenty of controversy surrounding the appropriateness of specific bills, such as the California bill. However, there was also pushback to the EU AI Act, which was nevertheless introduced. Thus, it is not clear whether the ongoing 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.
The impact of AI regulation
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.
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 spotlight 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.
With the incoming regulated landscape, businesses must evaluate the specific data they wish to collect with the help of a legal expert in the field when embarking on web scraping activities. 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 yet. Hence, continuous monitoring and adaptation of your AI usage are crucial.
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