Artificial intelligence is spreading fast enough to make ethics manuals look almost quaint. Governments therefore face a difficult task: setting basic rules for a technology that changes faster than regulation can keep pace.
On Monday, the South Korean government approved updated national AI ethics principles as generative AI becomes woven into everyday life and business.
The framework rests on three core values: human dignity, the common good and the sustainability of humanity. Seven principles put those values into practice for AI development and use: human-centeredness, privacy protection, fairness and inclusiveness, accountability, safety, reliability and transparency.
They apply across the AI life cycle, from development and provision to use and monitoring, and assign responsibilities not just to providers but to users, government and society. That is the framework’s most consequential shift.
The principles remain voluntary, leaving room for experimentation. Their practical value, however, will depend on whether general principles can become workable standards.
The extension of responsibility to users has practical merit. People are expected to check AI-generated results against their purpose and circumstances rather than accept them mechanically. They should avoid unnecessary sharing of personal information and refrain from using AI for privacy violations, false information or sexual deepfakes.
This reflects a simple reality: Even a well-designed system can be badly used.
Yet shared responsibility can also blur the line of liability. A consumer cannot reasonably be expected to compensate for biased training data, opaque system design or inadequate safeguards built into a platform.
When an AI-assisted decision causes harm, it must remain possible to distinguish between a developer’s failure and a user’s misuse. Otherwise, responsibility becomes everyone’s job and, in practice, nobody’s.
The same tension appears in transparency. The principles encourage providers to disclose information when copyrighted material is used in AI training, including its source and how it was used.
Korean AI companies have objected that detailed disclosure could expose proprietary information and weaken their competitive position. Training data can be a commercial asset, especially when domestic developers compete with much larger foreign firms.
The government’s answer is proportionality. Transparency should reflect the purpose and level of risk involved rather than impose identical disclosure requirements on every AI application.
For instance, a system influencing hiring or another consequential decision deserves far more scrutiny than a low-impact creative tool. That distinction can preserve legitimate trade secrets while focusing scrutiny where people’s rights and opportunities are at stake.
Still, non-binding principles have obvious limitations. They carry no penalties and do not replace legislation. Civic groups have rightly asked what recourse individuals have when an automated decision harms them, including how they can secure human review or challenge the decision.
The framework’s credibility will depend on how it works in the workplace and the marketplace. The government plans case-based guidance, sector-specific self-checklists and ethics materials. These should be concrete enough for a startup building a model, a company buying an AI system and a public agency procuring one to know what responsible use means in practice.
Published in Dawn, August 31st, 2026
