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Red teaming is adversarial testing: people try to make the model produce harmful, false, or off-policy output, and the findings feed back into training and guardrails. It differs from normal evaluation by being creative and open-ended rather than scored against a fixed set. For frontier models it is increasingly a regulatory expectation. In practice: Twenty people spending a week trying to make a support bot promise refunds it cannot honour. ...
Responsible AI is the organisational side of trustworthiness: who decides, who reviews, who is accountable when it goes wrong. It is largely a governance and process discipline rather than a technical one. Where it stays a values statement with no owner and no veto, it is decoration. In practice: A named owner who can stop a launch, not a slide about principles.
Robustness is performance outside the tidy test set: typos, edge cases, distribution shift, attack. A model that is accurate on clean data and collapses on real data is not accurate in any sense that matters. It is one of the trustworthiness characteristics in the NIST AI RMF and a requirement for high-risk systems in the EU. In practice: Accuracy drops from 94% to 51% when the input photos are taken in poor light. ...
Shadow AI is the AI version of shadow IT, and it is nearly universal because the tools are free and the productivity gain is immediate. The risks are data leaving the perimeter and unreviewed output entering work products. Blocking tools reliably fails; providing sanctioned ones plus training works better. In practice: Client contracts pasted into a free consumer chatbot to get a summary.
Transparency is about disclosure rather than mechanism: publishing model cards, documenting data, and telling people they are talking to a machine. The EU AI Act makes several forms of it mandatory, including labelling synthetic content and disclosing chatbot interactions. In practice: A banner stating that the assistant is AI and a human can take over.
Trustworthy AI is the framing NIST uses in the AI Risk Management Framework, listing characteristics a system should demonstrate rather than a single score to hit. The characteristics trade off against each other, so the work is balancing them in context. It is deliberately sector-agnostic. In practice: A system can be accurate and still untrustworthy if nobody can explain or contest it.
Watermarking biases generation so a hidden statistical or visual pattern can be detected later. It is a partial measure: image watermarks survive light editing but not determined removal, and text watermarks weaken under paraphrasing. The EU AI Act pushes providers toward machine-readable marking of synthetic content. In practice: A pattern in generated pixels that a detector can read but a viewer cannot see.