AI Disclosure
Stating that AI was used in producing a piece of work, where a policy, client or publisher requires it. Distinct from permission: some contexts allow AI use but require it to be declared.
Why it matters
Disclosure rules are now common in academic, editorial and client work, they vary a great deal, and the consequences of getting one wrong land on the person who did not check. Knowing that permission and disclosure are separate questions is the practical part.
A concrete example
A university may permit AI for research and outlining while requiring a statement of what was used and how, and prohibit it for the final text entirely. A client contract may say nothing. A journal may forbid it outright. All three are ordinary positions, and reading one and assuming the others is how people end up in a conversation they did not expect.
How to use it
Disclosure is a policy question rather than a technical one, and the practical advice is to find out the rule before you need it rather than after. Academic policies vary widely and many now require declaring AI use, with the requirement usually specific about what counts. Some clients write it into contracts. Some publishers prohibit it outright. Where a policy is ambiguous, ask rather than assume — assuming favourably is not a defence anyone accepts afterwards, and the conversation is much easier before submission than after an accusation.
The common mistake
Treating disclosure as equivalent to permission, or its absence as prohibition. They are separate: some contexts allow AI use but require you to say so, others prohibit it entirely, and a few require nothing. Reading one policy and generalising is how people get this wrong.
Related terms
AI Detector
A tool that estimates whether a passage of text was generated by an AI model, by measuring statistical properties of the writing rather than by checking any record of its origin.
Plagiarism Detection
Automated systems that compare text against existing published content to identify similarity. Critical for AI-generated content, which can inadvertently reproduce training data.
AI Alignment
The research challenge of ensuring AI systems pursue goals that are beneficial to humans. Misaligned AI could technically achieve its objective while causing unintended harm. Alignment research aims to make AI reliably helpful, harmless, and honest.
Agentic AI
AI systems that operate autonomously over extended tasks — planning, executing, and self-correcting without step-by-step human guidance. Unlike chatbots, agentic AI sets sub-goals, uses tools, and adapts its strategy based on intermediate results.
AI Agent
An autonomous AI system that can perceive its environment, make decisions, and take actions to achieve goals — like managing your email, scheduling meetings, or monitoring data.
ATS (Applicant Tracking System)
Software used by employers to filter and rank job applications by scanning resumes for keywords, formatting, and relevance. AI resume builders optimize output to pass ATS screening.