Why has AI Regulation Stopped Being Just a 'Tech Issue'?
With the spread of generative AI, AI has rapidly moved from being a tool for a subset of researchers to an infrastructure for society. From recruitment, credit decisions, healthcare, education, advertising, administrative procedures, and internal business process reforms, there are more and more situations where AI participates in decision-making and information flows. Consequently, the issues of safety, accountability, copyright, privacy, and bias are no longer just technical challenges but regulatory concerns as well.
What we should note here is that AI regulation around the world is not a monolith. Put simply, the EU tends to lay down rules first, the US moves forward with operation and guidance as it progresses, and Japan pursues a balance between innovation and risk management within a soft framework — these are the hues of the three directions. This article organizes these three dynamics into a single map.
EU: EU AI Act — The Core of Risk-Based Regulation
The EU AI Act classifies AI by its use and the magnitude of its impact (risk) and applies obligations in a phased manner, adopting a risk-based approach. The EU has previously led global personal data protection through GDPR, and in this case too, it is highly likely to affect even non-EU companies (the Brussels Effect).
Risk Categorization Concept (Image)
- Prohibition (Unacceptable risk): Uses that severely infringe on human freedom or safety may be prohibited in principle.
- High risk: Areas such as recruitment, education, critical infrastructure, healthcare, judiciary and administration, where the impact on daily life is large. Compliance requirements become heavier.
- Limited risk: Imposes certain obligations such as transparency to users.
- Minimal risk: Generally free to use.
Furthermore, in response to the spread of generative AI, the EU AI Act has focused on obligations for GPAI and for large-scale models (often called foundation models). The point is that responsibilities are designed to be shared between model providers (developers) and those who deploy or use the model in business (the deployment side).
Obligations That Really Matter in Corporate Practice (Representative Examples)
- Risk management: For high-risk uses, risk assessment and mitigation should be run as a process.
- Data governance: Managing the quality, bias, and legality of training and evaluation data.
- Technical documentation and logs: Documentation and operation logs that withstand authorities or audits.
- Transparency: Notifying that AI is being used and indicating that the outputs are generated, which is a central topic of discussion.
- Human oversight: Designing to avoid fully automatic decisions on important matters and ensuring the possibility of human intervention.
From a practical perspective, the EU AI Act asks not only about the technology together with its capabilities but also whether the organization can produce audit trails for development, provisioning, and operation. In addition to model performance evaluation, it is necessary to address explainability, robustness, security, and bias testing as part of a packaged set of requirements.
