The rise of generative AI (gen AI) in recent years has led to a push to establish regulatory frameworks for AI governance. This is a natural development, as the rise of AI appears to raise issues in data privacy and protection, bias and discrimination, security, 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 in 2021. We also have the EU’s AI law, which has just come into force and will come into full effect in 2026. Other jurisdictions are following their own steps to regulate the AI ecosystem.
Senior legal advisor at Oxylabs.
AI regulation in the EU versus other jurisdictions
The relative speed with which the EU has issued uniform regulations to govern the use of all types of AI makes it stand out. Other major jurisdictions, such as China and the US, appear to have taken a different approach. China is gradually regulating specific areas of AI, addressing what are recognized as risks. In 2021, they introduced regulations for recommendation algorithms, which by then have expanded their capabilities in digital advertising. Regulations followed in 2022 in the field of deep synthesis models or, in general terms, deepfakes and content generation. And then, in 2023, regulations on generative AI models were introduced as these models took off in commercial use.
The US has not yet introduced AI regulations at the federal level. There are rapidly evolving regulations at the state level, such as the California AI Act. However, it may take some time for them to come into effect, even after all legal hurdles have been cleared. Do regulations hold back innovation?
Stricter regulatory frameworks could impose AI compliance costs on companies and hinder competitiveness and innovation. On the other hand, they offer the benefits of protecting consumers and adhering to certain ethical standards.
Companies are reluctant and there is still a lot of controversy over the appropriateness of specific bills, such as the California bill. However, there was also resistance to the EU AI law, which was introduced anyway. So it is not clear whether the continued delay in the US is solely due to lobbyism or other obstacles in the legislative process. It may also be that some still view AI as a futuristic matter, not fully realizing the extent to which it is already a legal issue today.
The impact of AI regulation
AI regulation is not only focused on the development of AI, as it can cover various issues such as data privacy and protection, intellectual property, consumer protection, the deployment and use of AI, liabilities for non-compliance, and so forth. For example, the EU AI law also covers the use of AI in physical devices, such as elevators. Additionally, all companies that collect data for advertising are potentially affected, as AI regulations may also include algorithmic bias in targeted advertising.
AI has had a significant impact on the web scraping industry and will likely continue to do so. From data collection, validation, analysis or overcoming anti-scraping measures, there is a lot of potential for AI to vastly improve the efficiency, accuracy and adaptability of web scraping operations. Although it is too early to comment on this now, any regulations in the field of AI could accordingly impact the above-mentioned web scraping areas that also involve AI.
AI regulations may also put a spotlight on certain areas of law that have always been highly relevant to the web scraping industry, such as privacy or copyright laws. Ultimately, scrapping content protected by such laws without proper authorization can always lead to legal issues, and that now includes using AI in this way.
With the upcoming regulated landscape, companies should evaluate the specific data they want to collect with the help of a legal expert in the field when embarking on web scraping activities. When it comes to AI governance in general, it is important to recognize that the legal landscape of AI is very new and rapidly evolving, and there are not many precedents to refer to yet. That’s why continuous monitoring and adjustment of your AI usage is crucial.
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