Artificial intelligence regulation covers very different systems, from recommendation tools to software used in employment, credit and public services. A single technical rule is unlikely to fit every use, which is why many policy proposals begin with transparency.
Transparency can mean several things. A user may need to know that they are interacting with an automated system. A regulator may require documentation about training data, testing and known limitations. An affected person may need an explanation of how a decision was reached.
Disclosure alone is not enough. A company can publish a long technical report that few people can understand while leaving the practical risk unchanged. Information must be relevant to the audience and connected to a way of correcting harm.
Risk-based approaches try to apply stronger obligations to systems with greater consequences. The difficulty is defining risk before deployment and updating the assessment when a model is used in a new context.
Independent audits can add credibility, but audit quality depends on access, standards and incentives. A narrow test may miss discrimination, security failures or misuse that appears only in real operations.
Transparency is attractive because it supports many other forms of oversight. It does not decide what uses should be prohibited, but it creates evidence needed for enforcement, research and public debate.