9 Approaches for Artificial Intelligence Government Regulations
Since 2016, over thirty countries have passed laws that explicitly mention Artificial Intelligence. As of 2025, discussions regarding AI legislation in various legislative bodies have intensified globally. Various regulatory approaches have emerged, each with unique characteristics and implications for AI governance.
Principles-Based Approach
This approach offers stakeholders a set of fundamental propositions, or principles, providing guidance for the development and use of AI systems. These principles emphasize ethical, responsible, and human-centric processes that respect human rights. Notable examples include UNESCO’s Recommendations on the Ethics of AI and the OECD’s Recommendation of the Council on Artificial Intelligence.
Standards-Based Approach
In this approach, the state’s regulatory powers are delegated—either totally or partially—to organizations tasked with producing technical standards. These standards guide the interpretation and implementation of mandatory rules. For instance, Recital 121 of the EU’s AI Act highlights the importance of standardization in providing technical solutions for compliance, fostering innovation, and promoting competitiveness.
Agile and Experimentalist Approach
This approach generates flexible regulatory schemes, such as regulatory sandboxes, allowing organizations to test new business models under flexible conditions with oversight from public authorities. The EU’s AI Act exemplifies this by establishing a framework for regulatory sandboxes, enabling real-world testing of innovative AI systems.
Facilitating and Enabling Approach
The goal here is to create an environment that encourages all stakeholders to develop and use responsible, ethical, and human rights-compliant AI systems. UNESCO’s Readiness Assessment Methodology (RAM) aims to help countries gauge their preparedness for ethical AI implementation, pinpointing necessary institutional and regulatory changes.
Adapting Existing Laws Approach
This approach involves amending existing sector-specific and transversal rules to improve the regulatory framework incrementally. For example, Article 22 of the EU’s General Data Protection Regime (GDPR) asserts that individuals have the right not to be subjected to decisions based solely on automated processing, which significantly affects them.
Access to Information Mandates Approach
This approach requires transparency measures that allow public access to basic information about AI systems. Countries such as France have adopted algorithmic transparency obligations for public bodies, mandating the publication of rules defining the main algorithmic processes used in decision-making.
Risk-Based Approach
Regulations in this category establish obligations based on an assessment of the risks tied to specific AI tools in various contexts. An example is Canada’s Directive on Automated Decision-Making, which aims to minimize risks to clients and society while ensuring efficient decision-making aligned with Canadian law.
Rights-Based Approach
This approach focuses on establishing obligations to protect individuals’ rights and freedoms. A proposed human rights-based approach suggests empowering individuals and social groups in African countries to claim their rights while strengthening the capacity of duty-bearers to respect these rights.
Liability Approach
This approach assigns responsibility for problematic uses of AI systems, with specific penalties for non-compliance. The EU’s AI Act outlines penalties for infringements, including administrative fines that can reach up to €35 million or 7% of the total worldwide annual turnover, whichever is higher.
Understanding these diverse regulatory approaches is crucial for stakeholders involved in the development and governance of AI technologies. As the landscape of AI regulation continues to evolve, keeping abreast of these frameworks will be essential for fostering innovation while safeguarding ethical standards and human rights.
