Perspectives

Policy frameworks are essential for guiding the development of AI and emerging technologies in ways that benefit society while minimizing risks. But, they walk a fine line. To be effective, these frameworks must strike a balance between encouraging innovation and implementing safeguards that address both immediate concerns and long-term impacts. At the same time, too much regulation risks stifling progress, while too little could leave vulnerable populations unprotected from unintended consequences. Experts and experienced tech leaders have been leading on the debates on the implications of AI regulation. But it is also important for everyone involved in the industry to contribute to the conversation, as the topic is so vital for businesses, academia, and society as a whole.

With that in mind, let us look at the bigger picture and align on a first entry point to the conversation. So, what do effective AI policy frameworks look like and how can we use them to chart a course toward advancement aligned with shared values and priorities?

Five Key Pillars of Responsible AI Policy

First, a smart starting point is risk-based regulation. This approach adjusts the level of oversight based on how much impact an AI system could have, with a real-world policy example being the EU AI Act. In this approach, low-risk applications may need little supervision, high-stakes systems that affect health, safety, or fundamental rights face more rigorous checks, and some AI uses with unacceptable risk, line mass real-time surveillance or social scoring, would be prohibited. This helps avoid stifling harmless innovations while ensuring that more powerful systems are properly controlled.

Second, transparency is another cornerstone of responsible AI policy. Developers and companies should be required to provide clear, accessible information about how their systems work, what data they use, and what their limitations are. However, it’s not enough to just publish technical details that most people can’t understand. Instead, policies should focus on making the right information available to the right audiences in a way they can easily grasp, for regulators, users, and the public alike.

Third, fairness and bias are also major concerns, given AI systems can unintentionally reinforce discrimination when trained on biased data. Strong policies must require testing for bias before deployment, continuous monitoring after launch, and effective ways to address any harm that occurs. These safeguards are especially important for systems making decisions about jobs, loans, healthcare, or legal rights.

Fourth, the quality and governance of data used to train AI systems is equally vital. Rules are needed to ensure data is collected and used responsibly, with clear consent, strong privacy protections, and secure handling, as set out by the EU General Data Protection Regulation, for example. At the same time, frameworks should encourage responsible data sharing to improve AI performance, perhaps by supporting trusted data commons or secure data trusts with proper oversight.

Finally, accountability is the backbone of any strong policy framework. With AI systems often operating in complex, semi-autonomous ways, it’s crucial to clarify who is responsible when things go wrong. Regulations should require human oversight for key decisions, set standards for audits and transparency, and update liability rules to cover modern risks.

The Toolkit for Governments to Foster Responsible AI

These five pillars are central to foster responsible AI, but how can governments embed them best in regulation? First, global cooperation is key, even if certain regions, like the EU, will be regulatory front-runners. As AI technologies are deployed globally, international coordination becomes increasingly important. Without it, we risk creating a fragmented landscape with different rules, inconsistent protections, and opportunities for companies to shop around for the most lenient regulations. Global cooperation (through mutual recognition, shared standards, and joint enforcement efforts) can help solve these challenges.

Second, supporting research is a powerful tool. Governments and institutions should invest in research on AI safety, transparency, and alignment with human values. Some countries are already creating dedicated research centers to focus on these goals, combining policy insights with technical innovation.

Why Stakeholders Need to Make Themselves Heard

As AI and emerging technologies reshape the world around us, well-designed policy frameworks offer a path forward. They can help ensure these tools reflect our values, respect human rights, and spread their benefits broadly so that the future we build with AI is one we all want to live in.

Crucially, policy frameworks must be flexible and forward-looking. Technology changes fast, and static rules can quickly become outdated. The best frameworks include regular reviews, horizon scanning for emerging issues, and experimentation through regulatory sandboxes. This allows policymakers to stay ahead of the curve while staying grounded in core ethical principles.

This can only work by engaging a wide range of voices in the policymaking process. AI affects different groups in different ways, and policies made without input from diverse stakeholders risk missing important perspectives. Ongoing dialogue with technical experts, impacted communities, civil society, and industry leads to smarter, more inclusive, and more effective regulations.

The same applies to corporations affected by the implementation of regulation. Clear policy frameworks bring benefits to businesses, as they can profit from strong, transparent and trustworthy standards that will speak to their customers. Especially in a field like AI, where its implementation affects so many people and a wide range of fields, clear regulation and its implementation can go a long way to build a strong corporate reputation. Yet dialogue goes in both directions. Businesses are the ones that are affected by regulation most directly and should also engage in an open dialogue with regulators to work on making regulations more practical and targeted. Good examples of this are the ongoing work around EU AI governance, such as the General-Purpose AI Code of Practice, or the EU AI Pact.

Trilligent specializes in this work. Our Brussels and international policy experts excel in understanding your issues, breaking them down and making them digestible for a specific audience of decision makers, and developing strategies on how to conduct a productive dialogue with diverse stakeholders to build mutual understanding. Feel free to reach out at contactus@trilligent.com to learn how the team can help your organization navigate the ever-evolving regulatory landscapes.