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43 I L T A N E T . O R G global development and deployment of these technologies. This highlights the need for international cooperation and harmonization in the development of data and AI regulations. Furthermore, there is a growing recognition that traditional forms of regulation may not be sufficient to address the unique challenges posed by data and AI. Traditional regulations tend to be reactive, responding to harms after they have occurred. But with data and AI, there is a need for proactive regulation that can anticipate and prevent harms before they occur. This requires a shift towards more dynamic and flexible forms of regulation, such as risk-based regulation, which focuses on managing the risks associated with data and AI, rather than prescribing specific behaviors or technologies. As cited by the European Parliament, "The EU should not always regulate AI as a technology. Instead, the level of regulatory intervention should be proportionate to the type of risk associated with using an AI system in a particular way." There is also a need for more inclusive and participatory forms of regulation. Given the broad societal impacts of data and AI, it is important that all stakeholders – including businesses, civil society groups, and the public at large – have a say in how these technologies are regulated. This can be achieved through mechanisms such as public consultations, multi-stakeholder forums, and citizen juries, which can provide diverse perspectives and insights on the regulation of data and AI. Finally, there is a need for greater regulatory capacity and expertise. Regulating data and AI requires a deep understanding of these technologies and their societal implications. This requires investing in regulatory capacity building, such as training for regulators, the creation of specialized regulatory agencies, and the development of interdisciplinary research and expertise in data and AI regulation. Balancing Regulation and Innovation Balancing the need for regulation with the desire for innovation is a delicate task. On the one hand, we need robust regulations to protect privacy and ensure ethical AI use. On the other, we need to avoid overly restrictive rules that could stifle innovation and economic growth. Striking the right balance is critical, but it is also incredibly challenging. Regulation is essential to ensure that the use of data and AI aligns with societal values and norms. It can provide a framework for ethical behavior, set boundaries for acceptable use, and protect individuals and societies from potential harm. However, regulation can also hinder innovation if it is too restrictive or not well-designed. It can create barriers to entry, limit the development and deployment of new technologies, and stifle creativity and experimentation. "There is also a need for more inclusive and participatory forms of regulation."

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