AI Disclosure

AI disclosure is telling the people affected by a system that AI was involved — that they are talking to a bot rather than a person, that a piece of content was generated or assisted, or that a decision about them was made with model input. It is partly a legal question and largely a trust one, and the two point in the same direction more often than product teams expect. The cases divide usefully. Conversational disclosure, where a user needs to know whether the reply came from a person, matters most when the alternative is being misled about who is on the other end and when the user may want to escalate to a human. Content disclosure covers generated text, images, audio and video, and is where synthetic media of real people raises the sharpest issues. Decision disclosure covers automated processing that affects someone's access to a service, employment or money, and is the area most likely to carry a statutory obligation depending on the jurisdiction. How it is worded decides whether it helps. A blanket footer saying the site uses AI is noise: it appears everywhere, tells a reader nothing about the answer in front of them, and dilutes the cases that matter. A disclosure attached to the specific output, saying what was generated and what a human checked, is usable — a reader can decide how much weight to give it. Where the answer is drawn from sources, showing them beats asserting reliability. There is a version that backfires. Disclosure used as a liability shield — a long notice saying the output may be inaccurate, attached to a product presented as authoritative — transfers the burden to the user without changing anything about the system, and readers learn to ignore it. If a feature is unreliable enough to need a warning that strong, the warning is not the fix. Decide the policy once and apply it consistently, because the damage from disclosure is almost never in disclosing. It is in a customer discovering, from a support agent's slip or a journalist's story, that something they treated as human or hand-made was not. Consistency also makes the rule easy for staff to follow, which is what keeps it true across a product where AI is being added feature by feature.