High-risk AI systems are artificial intelligence applications that have significant potential to impact people’s lives, health, safety, or fundamental rights. These systems are often subject to stricter legal and ethical requirements because their misuse or failure can lead to serious consequences.
High-risk AI systems matter because they are subject to legal scrutiny and carry heavy ethical responsibilities. Governments, regulators, and compliance officers need to understand what qualifies as high-risk and how these systems must be managed to avoid harm, penalties, or reputational damage.
“71% of citizens in Europe say they would not trust an AI system to make decisions about their welfare benefits or job applications.”
(Source: European Commission Flash Eurobarometer 2022)
What qualifies as high-risk AI
High-risk AI systems are defined by specific use cases that could negatively affect people or society. The EU AI Act outlines several areas, such as employment, education, law enforcement, and access to essential services, where AI is automatically labeled as high-risk.
Examples include:
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AI systems used to evaluate creditworthiness
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Proctoring tools in remote education
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Facial recognition tools in public spaces
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Automated job interview scoring tools
Any AI system used in these contexts must meet strict safety, transparency, and oversight requirements. Violations can result in major fines or bans on use.
Real-world examples of high-risk systems
One example is the Dutch government’s use of the “SyRI” system, which attempted to predict welfare fraud using personal data. The system was later ruled unlawful because it disproportionately affected low-income neighborhoods and lacked transparency.
Another case involved automated proctoring software during university exams. Students raised serious concerns about false cheating accusations, privacy violations, and algorithmic bias against darker skin tones.
These examples highlight how high-risk AI systems can go wrong when built without accountability and tested without diverse data.
Risk classification and compliance obligations
The EU AI Act defines four risk levels: unacceptable, high-risk, limited risk, and minimal risk. High-risk systems are subject to legal controls, while unacceptable systems (such as social scoring by governments) are banned.
Organizations deploying high-risk systems must:
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Conduct a risk assessment before use
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Maintain logs and documentation
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Inform users of their rights
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Ensure human oversight
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Apply quality controls and testing
Compliance teams must align with frameworks such as ISO/IEC 42001, which provides a management system for AI governance. This framework supports organizations in managing risks, defining policies, and auditing AI systems against compliance rules.
Best practices for managing high-risk AI
Managing high-risk AI systems starts with a mindset of accountability and early risk detection. Companies must prepare for legal scrutiny by designing systems that are explainable, tested, and documented.
Key practices include:
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Start with a risk register: Classify all AI systems used in the company by their risk level.
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Build diverse teams: Involve legal, technical, and domain experts to evaluate system impact.
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Use model cards and datasheets: Document the model’s purpose, limitations, and data sources.
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Test for fairness and performance: Apply scenario-based testing across different user groups.
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Enable human review: Ensure a person can override the AI’s decisions in sensitive areas.
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Review vendor systems: Ask third-party providers to share audit logs and bias test results.
Strong documentation and training programs help teams understand the consequences of failure and their role in reducing risk.
FAQ
What makes an AI system “high-risk”?
A system is high-risk if it can affect health, safety, human rights, or access to critical services. This is often defined by its use case, not its technical complexity.
Can a chatbot be considered high-risk?
Yes, if it is used for purposes like psychological support, legal advice, or education in sensitive environments, it may be classified as high-risk depending on how it is deployed.
Who is responsible for compliance in a high-risk AI system?
Responsibility is shared across the provider and the deployer. The provider ensures the system is built according to regulations, while the deployer must ensure the AI is used within allowed contexts and is properly monitored.
How often should risk assessments be done?
Risk assessments should be completed before the system is deployed and repeated when the system is updated or used in a new context.
Are open-source systems also regulated?
Yes. Even if an AI model is open-source, once it is deployed in a high-risk context, the deployer becomes responsible for ensuring it meets the required legal and ethical standards.
Summary
High-risk AI systems must be managed with care, especially when they influence areas like health, education, employment, and security. They require strong safeguards, transparent design, and frequent audits. Regulators are watching closely, and mistakes can lead to serious harm or legal penalties. Responsible organizations understand the stakes and take proactive steps to reduce risk before deploying these systems.
