AI law: Council and European Parliament agree on the world's first AI rules: Implications on Risk Management
Ronaldo Rossi da Costa
Actuary | MIBA | Risk Management | Insurance | IFRS 17 | ORSA | SAS Educator
The main idea is to regulate AI based on the latter’s capacity to cause harm to society following a ‘risk-based’ approach: the higher the risk, the stricter the rules. As the first legislative proposal of its kind in the world, it can set a global standard for AI regulation in other jurisdictions, just as the GDPR has done, thus promoting the European approach to tech regulation in the world stage.
The AI Law from Council of the European Union is a legislative initiative that aims to regulate AI systems placed on the European market and used in the EU to ensure that they are safe and respect fundamental rights and EU values. The regulation is based on a risk-based approach, i.e. the greater the risk, the stricter the rules. The AI law is the first legislative proposal of its kind in the world and could set a global standard for the regulation of AI in other jurisdictions, just as the GDPR did. The law can promote the development and adoption of safe and reliable AI across the EU single market, by both private and public actors. The AI law will not apply to systems used exclusively for military or defense purposes.
According to the EU Law on AI, AI systems are considered high risk if they can have a significant impact on a user's life chances. The law describes eight types of systems that fall into this category, including those used in healthcare, transportation, energy and parts of the public sector. These systems are subject to strict obligations and must undergo conformity assessments before being placed on the EU market. The European Parliament proposes changes to the category, suggesting that AI systems covered by the listed uses should only be considered high risk if they pose a significant risk to health, safety, or fundamental rights.
Based on this, a revised risk model governance and management system is mandatory to identify, assess and mitigate risks associated with the use of AI and machine learning models. It is a subset of operational risk and is concerned with the potential adverse consequences of decisions based on incorrect or misused models (inaccurate premiums or poorly assessed solvency capital requirements). Model risk can be reduced with model management such as testing, governance policies, and independent review.
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