01 — Working definition
What Ethicality means by AI.
Artificial intelligence is the science and engineering of building intelligent machines — computer systems that perform tasks associated with human intelligence, such as understanding language, recognising images, learning from data, reasoning, and making decisions.
Modern AI typically learns by finding patterns in large amounts of data and using those patterns to generate predictions or responses. It can be narrow, suited to a specific task, or general-purpose, as with large language models that handle many.
Ethicality specialises in the certification of large language models, generative and foundation models, and the agentic systems built on top of them.
02 — Families in scope
Six families. One standard.
01
Predictive ML
Statistical models trained on historical data to predict an outcome.
e.g. Credit scoring, churn prediction, diagnostic classifiers.
02
Computer Vision
Models that interpret images, video, or sensor data.
e.g. Facial recognition, medical imaging, autonomous perception.
03
Natural Language
Models that read, classify, or generate human language.
e.g. Translation, summarisation, sentiment, search ranking.
04
Generative & Foundation Models
Large pre-trained systems that produce new text, images, audio, code, or actions.
e.g. LLMs, diffusion models, multimodal assistants, agents.
05
Decision & Optimisation
Systems that rank, recommend, or allocate at scale.
e.g. Recommender systems, dynamic pricing, allocation, routing.
06
Autonomous Control
Systems that take real-world actions with limited human approval per decision.
e.g. Robotics, vehicles, industrial control, trading systems.
03 — Why AI is different
Five properties that set AI apart from ordinary software.
- Trained, not programmed
- Behaviour is learned from data rather than written as rules. The training data is part of the system.
- Statistical, not deterministic
- Outputs are probabilistic. Identical inputs can produce different outputs across versions or sessions.
- Opaque by default
- The path from input to output is not directly inspectable without dedicated interpretability work.
- Consequential at scale
- Decisions are made or influenced for many people simultaneously, often without per-decision review.
- Lifecycle-bound
- Risk is created across data sourcing, training, evaluation, deployment, monitoring, and retirement — not at one point.
04 — Out of scope
What EUMS does not treat as AI.
Pure rules-based software, deterministic calculators, basic data pipelines, and conventional analytics dashboards are not considered AI for the purposes of EUMS.
When these systems are wrapped around an AI model — for example, a rules engine filtering LLM output — the combined system is in scope.
EUMS § 2.4 — Definitional disputes are resolved by the Standards Committee.
05 — From definition to certification
If your system fits the definition, EUMS applies.
Read the standard, or start an application.