Meaningful human oversight
Oversight that actually enables a person to understand the system's limits, stay alert to automation bias, interpret the output correctly, decide not to use it, and intervene or stop it, as required for high-risk systems by the EU AI Act Article 14 (Regulation (EU) 2024/1689). The opposite of a costume.
Defined in 2 GAGE programs, which carry 3 distinct definitions of it. The wording above is taught in AI Governance: Applied Mastery.
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
Oversight that actually enables a person to understand the system's limits, stay alert to automation bias, interpret the output correctly, decide not to use it, and intervene or stop it, as required for high-risk systems by the EU AI Act Article 14 (Regulation (EU) 2024/1689). The opposite of a costume.
The EU AI Act requirement, defined by capability rather than title, that a human can understand a high-risk system's limits, resist automation bias, interpret its output, override or reverse it, and stop it. A rubber-stamp approval does not satisfy it.
A human who can see why an AI system decided and can override it, not a rubber stamp on a ranked list. Decorative oversight leaves the tool as the real decider.
Where it is taught
The exact lessons this term appears in. The first 7 topics of every program are free with a free account.
- Regulatory Landscapes: AI Laws and Governance Frameworks · Ethical and Responsible AI and Operational Governance, AI Literacy & Professional Conduct
- The hiring-AI problem: bias audits, notices, and the law already watching hiring AI · The EU AI Act: The Executive Map, AI Governance: Applied Mastery
- The oversight pattern: human-in-the-loop, on-the-loop, and out-of-the-loop, chosen per task with reasons · Agents Under Command, AI Governance: Applied Mastery
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.