Reflection on the future of Artificial Intelligence regulation is conditioned by the fact that this technology is advancing at an extraordinarily rapid pace. Today we know that we are moving towards AI that is increasingly closer to human intelligence, agentive, contextual, emotional and culturally shaped. An AI that will act as an invisible, omnipresent infrastructure, integrated into our daily routines, and that will function as a cognitive companion capable of making decisions on our behalf. In this scenario, regulation should not be based on how it works, regulating according to its internal processes, but rather on the consequences it may produce, and therefore it must become much more dynamic, technical and continuous. Regulation will have to adapt to a reality where AI is not a product, but an infrastructure. Supervision must be permanent, based on real-time data and automated auditing using algorithms capable of monitoring and explaining other algorithms. The requirements of transparency, traceability, natural explainability and continuous risk assessment must form the basis of the new regulatory framework. It is important to raise the level of discourse on risks, looking not only at the micro level, but also at the macro level: society, culture, politics, democracy and also the individual as a free agent. At the same time, equal and non-discriminatory access to technology must be provided if we do not want to have first-, second- or third-class citizens in areas such as agentive AI or neurotechnology.
Differences in regulation between different countries or regions
Differences between regions reflect distinct views on the role of the state, technology, and fundamental rights. The European Union favors a protective framework focusing on individual rights and risk management, while the United States employs a sectoral approach emphasizing private innovation. China, conversely, adopts a centralized model geared towards control, national security, and productivity. Despite these varied approaches, all regions face the shared challenge of regulating AI without impeding its rapid deployment and development.
Why this technology needs to be regulated
Regulating AI is crucial as it's a technology that significantly amplifies human capabilities, makes decisions with real-world impact, and operates in sensitive domains such as health, employment, education, security, and fundamental rights. AI possesses immense transformative potential, necessitating a regulatory framework that ensures fairness, transparency, security, respect for privacy, and non-discrimination. The goal is not to stifle innovation but to build societal trust that AI development adheres to clear ethical and legal boundaries. The emergence of agentic AI models further underscores the need to reformulate regulation, requiring new individual rights and obligations for developers and operators to safeguard personal autonomy and cognitive integrity in conjunction with AI and neurotechnology.
What are the pros and cons of regulating Artificial Intelligence?
AI regulation aims to effectively protect individuals, society, and democratic models by establishing limits and safeguards against abuse, discrimination, and opaque decision-making. In an increasingly AI-pervasive world, a robust, flexible, and accountable framework of trust is essential. However, regulation must also avoid hindering innovation and technological progress, as AI promises significant advancements in fields like health, science, security, and the environment. The challenge lies in regulating technologies that evolve faster than legislative processes, which can cause market distortions. Therefore, future regulation must be adaptable, based on continuous governance, and flexible mechanisms.
How future regulation will differ from current regulation
Future AI regulation will be distinct as it will need to oversee systems that continuously learn, interact, self-adapt, and communicate. This shift will move away from one-off assessments towards continuous supervision, algorithmic auditing, transparency, and traceability across the system’s life cycle. The regulatory framework will also involve the use of supervisory AI to explain and evaluate other AI systems, a concept still in its early stages. Furthermore, the internet and human-computer interactions will evolve, necessitating ethical and semantic interoperability protocols for intelligent agents and platforms. Responsibilities throughout the AI value chain, from model providers to end operators, will require clearer definitions, resulting in a more active, technical, and dynamically integrated regulatory approach.
The challenges facing AI regulation
AI regulation faces several critical challenges. The first is technical: regulating a constantly evolving system demands flexible mechanisms, real-time auditing, continuous risk assessments, and regulatory structures capable of comprehending the technology's inherent complexity. The second is institutional: regulators and supervisory authorities require enhanced capabilities, resources, and tools to effectively oversee an ecosystem increasingly dominated by large-scale intelligent agents. The third is global: preventing regulatory fragmentation is crucial, as incompatible rules across countries could hinder interoperability between intelligent agents and complicate effective global supervision. Finally, there's a significant social and political challenge: ensuring that new individual rights, such as disconnection, explainability, and portability, are translated into practical and effective mechanisms. Beyond merely mitigating risks, future regulation must also proactively anticipate the political, social, cultural, and cognitive impacts of ubiquitous AI, promoting its development to foster a better society and ensure technological progress benefits all segments of society, especially the most disadvantaged.
