Label AI-generated content

Why label AI-generated content?

Your audience has a right to know when the content they are reading was created or significantly shaped by AI. This is the principle behind Article 50 of the EU AI Act, which introduces transparency obligations for organizations publishing AI-generated or AI-modified content for public audiences. The Act provides an exemption for content that has undergone human editorial review, specifically when an individual assumes editorial responsibility for the publication. It is up to your organization and legal team to assess whether this exemption applies to your content and operations. That said, establishing a labeling practice now is sensible regardless. It gives your team visibility into how broadly AI is being used across your content operations, and it prepares you for a regulatory landscape that is likely to get more specific, not less.

Choose your approach

Kontent.ai’s flexible content model lets you implement AI content labeling in a way that fits your project. Two options work well here: a multiple choice element or a taxonomy group.

Multiple choice element

With multiple choice element, you define the options directly in your content type. It’s best suited for projects with a single content type requiring AI labeling, or situations where you don’t need to filter or report on AI-generated content regularly. Advantages:

• Quick to set up – Define the options once in your content type, and you’re done.
• Immediately visible – When the AI Agent works with the content type, it will see all available options without needing to reference anything external.
• Predictable behavior – The AI Agent sees the options clearly and can reliably select the appropriate value.

Considerations:

• No filtering in content inventory – You can’t filter your content items by multiple choice values in the UI, making it difficult to find labeled content for auditing or reporting purposes.
• Not reusable – If you need the same AI disclosure categories for other content types, you’ll need to recreate the multiple choice element for each one.
• No centralized updates – If labeling requirements change and you need to add a new category, you need to update each content type individually.

Taxonomy group

A taxonomy group defines a reusable set of terms that can be referenced by multiple content types. Best suited for organizations that need to track, filter, and report on AI-generated content across multiple content types, especially when compliance auditing is important. Advantages:

• Filtering and reporting – You can easily filter content items in the content inventory by taxonomy terms, making auditing and compliance reporting straightforward.
• Reusable across content types – Create the taxonomy once and use it in Articles, Blog posts, Case studies, or any other content type that needs AI labeling.
• Centralized management – Update the taxonomy in one place, and changes apply everywhere it’s used.
• Hierarchical categorization – If your disclosure requirements become more nuanced, such as “AI-assisted > with human review” vs. “AI-assisted > without human review”, taxonomies support nested structures.

Considerations:

• Requires additional setup for the AI Agent reliability – Since the content type only stores a reference to the taxonomy group, the AI agent needs to fetch that taxonomy to see available terms. This means it’s less likely to proactively suggest categorization unless explicitly instructed.

Make labeling work reliably