Term tag: Regulation

AI Governance (AIG)

Which AI rules apply to an organisation — by role, risk tier and jurisdiction — and what compliance requires: inventories, documentation, release gates.

AI Healthcare

Where AI is actually deployed in medicine — imaging, triage, ambient notes — and why 'beats doctors' headlines rarely survive contact with a clinic.

AI in Finance

Machine learning that approves loans and blocks card fraud — where you ship the most accurate model you can defend, not the best one you can build.

Ambient Clinical Documentation

AI scribes draft the clinical note from consultation audio. What the largest deployment and the randomised trials measured — and who stays liable.

Explainable AI (XAI)

What being explainable actually requires: a model you can read versus a post-hoc guess about one, and the evidence that the guess is often wrong.

FLOPs (Floating Point Operations)

The unit AI compute is measured in — how a training run's FLOP budget is counted, why FLOP and FLOPS are not the same, and why regulators use the number.

Machine Unlearning

Removing the influence of specific training data from an already-trained model without full retraining, driven by erasure law, copyright, and safety.

Model Card

A model card is a short document shipped with a released AI model stating its intended use, training data, and evaluation broken down by group.

Privacy

In AI, privacy is about what a model can memorize and leak about people, and the techniques—differential privacy, federated learning—that limit it.

Transparency

Transparency in AI is the degree to which a system's data, workings, and decisions are visible and disclosed to users, auditors, and regulators.

Watermarking (SynthID)

AI watermarking hides a detectable signal inside model-generated text and images. How SynthID works, its robustness limits, and the EU AI Act rules.