The input data this deployed system processes when it operates. Distinct from training data.
Sensor readings — air quality, temperature, sound level, energy, water flow. The AI takes in numeric measurements from physical sensors, usually with no person attached. Examples: PM2.5 readings from an air-quality monitor, decibel levels from a sound sensor, kilowatt-hours from a meter.
Locations, routes, surroundings, or floor plans. The AI takes in data describing where something is. Examples: GPS coordinates from a phone, foot-traffic counts on a sidewalk, a map of building corridors.
Taps, choices, paths walked, dwell time, purchases, or queries. The AI takes in records of what people did. Examples: tapped destinations on a wayfinding kiosk, items added to a cart, a search query typed into a public terminal.
Faces, fingerprints, voice prints, gait, gestures, gaze, posture. The AI takes in biological signals from a person's body. Examples: a face captured at a turnstile, a voice recorded by a kiosk microphone, gait analyzed by an overhead camera.
An eligibility, classification, ranking, or yes/no that another system already made about a person. The AI takes in this prior decision and uses it as input. Examples: a credit score from another model, an access-allow flag from upstream, a triage class assigned earlier in the pipeline.
Text, images, audio, or video that another AI made. The AI takes in this synthetic content and processes it further. Examples: an LLM-written summary fed into a translator, a generated photo passed to a moderation model, a text-to-speech clip routed to an audio system.
Schedules, routes, budgets, occupancy counts, public records, or other administrative data — not about any one person. The AI takes in data describing how a place or service runs. Examples: school occupancy by hour, trash-collection routes, a transit timetable, a department budget.
A signal that something in the physical world changed — a door opening, a light turning on, an alert sounding. The AI takes in evidence of these changes from upstream actuators or sensors. Examples: a turnstile-unlock event, an HVAC adjustment recorded by a sensor, a public-address alert played upstream.
A suggestion, forecast, risk score, or ranking produced earlier by another system. The AI takes in this advisory output and uses it. Distinct from a binding decision because it advises rather than determines. Examples: a forecasted demand curve, a risk score from a prior model, a recommended route from a navigation service.
Health, finances, beliefs, sexuality, immigration status, or other personal information that carries legal and social risk. The AI takes in this kind of protected data. Examples: insurance eligibility at a clinic kiosk, a benefits-application status, a self-declared pronoun.
Live API response from GET /schemas/ai@2026-05-06-beta/categories (this category) and GET /schemas/ai@2026-05-06-beta/elements?category_id=input_dataset.
{
"id": "input_dataset",
"name": [
{
"locale": "en",
"value": "Input Dataset"
}
],
"description": [
{
"locale": "en",
"value": "The input data this deployed system processes when it operates. Distinct from training data."
}
],
"prompt": [
{
"locale": "en",
"value": "What input data does this AI system process?"
}
],
"authoring_guidance": [],
"examples": [],
"sources": [],
"required": false,
"order": 6,
"datachain_type": "ai",
"shape": "circle",
"element_variables": [
{
"id": "additional_description",
"label": [
{
"locale": "en",
"value": "Description"
}
],
"required": true
}
],
"element_context": {
"id": "pii",
"name": [
{
"locale": "en",
"value": "Personal Information"
}
],
"description": [
{
"locale": "en",
"value": "How identifiable the data is when this system processes it. The element name says what the data is about; this colour says how directly it identifies a person."
}
],
"values": [
{
"id": "de_identified",
"name": [
{
"locale": "en",
"value": "Anonymized data"
}
],
"description": [
{
"locale": "en",
"value": "Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual."
}
],
"color": "#4A90D9"
},
{
"id": "pseudonymous",
"name": [
{
"locale": "en",
"value": "Pseudonymous data"
}
],
"description": [
{
"locale": "en",
"value": "Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information."
}
],
"color": "#9575CD"
},
{
"id": "identifiable",
"name": [
{
"locale": "en",
"value": "Identifiable data"
}
],
"description": [
{
"locale": "en",
"value": "The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing."
}
],
"color": "#FFD700"
}
]
}
}[
{
"id": "input_about_a_measurement",
"category_id": "input_dataset",
"title": [
{
"locale": "en",
"value": "About a measurement"
}
],
"description": [
{
"locale": "en",
"value": "Sensor readings — air quality, temperature, sound level, energy, water flow. The AI takes in numeric measurements from physical sensors, usually with no person attached. Examples: PM2.5 readings from an air-quality monitor, decibel levels from a sound sensor, kilowatt-hours from a meter."
}
],
"authoring_guidance": [],
"examples": [],
"sources": [],
"symbol_id": "values_time",
"variables": [
{
"id": "additional_description",
"label": [
{
"locale": "en",
"value": "Description"
}
],
"required": true
}
],
"shape": "circle",
"icon_variants": [
"default",
"dark",
"de_identified",
"de_identified.dark",
"pseudonymous",
"pseudonymous.dark",
"identifiable",
"identifiable.dark"
]
},
{
"id": "input_about_a_place",
"category_id": "input_dataset",
"title": [
{
"locale": "en",
"value": "About a place"
}
],
"description": [
{
"locale": "en",
"value": "Locations, routes, surroundings, or floor plans. The AI takes in data describing where something is. Examples: GPS coordinates from a phone, foot-traffic counts on a sidewalk, a map of building corridors."
}
],
"authoring_guidance": [],
"examples": [],
"sources": [],
"symbol_id": "spatial",
"variables": [
{
"id": "additional_description",
"label": [
{
"locale": "en",
"value": "Description"
}
],
"required": true
}
],
"shape": "circle",
"icon_variants": [
"default",
"dark",
"de_identified",
"de_identified.dark",
"pseudonymous",
"pseudonymous.dark",
"identifiable",
"identifiable.dark"
]
},
{
"id": "input_about_behaviour",
"category_id": "input_dataset",
"title": [
{
"locale": "en",
"value": "About behaviour"
}
],
"description": [
{
"locale": "en",
"value": "Taps, choices, paths walked, dwell time, purchases, or queries. The AI takes in records of what people did. Examples: tapped destinations on a wayfinding kiosk, items added to a cart, a search query typed into a public terminal."
}
],
"authoring_guidance": [],
"examples": [],
"sources": [],
"symbol_id": "about_behaviour",
"variables": [
{
"id": "additional_description",
"label": [
{
"locale": "en",
"value": "Description"
}
],
"required": true
}
],
"shape": "circle",
"icon_variants": [
"default",
"dark",
"de_identified",
"de_identified.dark",
"pseudonymous",
"pseudonymous.dark",
"identifiable",
"identifiable.dark"
]
},
{
"id": "input_biometric",
"category_id": "input_dataset",
"title": [
{
"locale": "en",
"value": "Biometric"
}
],
"description": [
{
"locale": "en",
"value": "Faces, fingerprints, voice prints, gait, gestures, gaze, posture. The AI takes in biological signals from a person's body. Examples: a face captured at a turnstile, a voice recorded by a kiosk microphone, gait analyzed by an overhead camera."
}
],
"authoring_guidance": [],
"examples": [],
"sources": [],
"symbol_id": "biometric",
"variables": [
{
"id": "additional_description",
"label": [
{
"locale": "en",
"value": "Description"
}
],
"required": true
}
],
"shape": "circle",
"icon_variants": [
"default",
"dark",
"de_identified",
"de_identified.dark",
"pseudonymous",
"pseudonymous.dark",
"identifiable",
"identifiable.dark"
]
},
{
"id": "input_decision",
"category_id": "input_dataset",
"title": [
{
"locale": "en",
"value": "A decision about you"
}
],
"description": [
{
"locale": "en",
"value": "An eligibility, classification, ranking, or yes/no that another system already made about a person. The AI takes in this prior decision and uses it as input. Examples: a credit score from another model, an access-allow flag from upstream, a triage class assigned earlier in the pipeline."
}
],
"authoring_guidance": [],
"examples": [],
"sources": [
{
"type": "regulation",
"title": "EU AI Act Article 3(1) — output classes (predictions, content, recommendations, decisions)",
"url": "https://eur-lex.europa.eu/eli/reg/2024/1689/oj",
"citation": "Regulation (EU) 2024/1689, Article 3(1)."
}
],
"symbol_id": "dm_accept-or-deny",
"variables": [
{
"id": "additional_description",
"label": [
{
"locale": "en",
"value": "Description"
}
],
"required": true
}
],
"shape": "circle",
"icon_variants": [
"default",
"dark",
"de_identified",
"de_identified.dark",
"pseudonymous",
"pseudonymous.dark",
"identifiable",
"identifiable.dark"
]
},
{
"id": "input_generated_content",
"category_id": "input_dataset",
"title": [
{
"locale": "en",
"value": "Generated content"
}
],
"description": [
{
"locale": "en",
"value": "Text, images, audio, or video that another AI made. The AI takes in this synthetic content and processes it further. Examples: an LLM-written summary fed into a translator, a generated photo passed to a moderation model, a text-to-speech clip routed to an audio system."
}
],
"authoring_guidance": [],
"examples": [],
"sources": [
{
"type": "regulation",
"title": "EU AI Act Article 3(1) — output class \"content\"",
"url": "https://eur-lex.europa.eu/eli/reg/2024/1689/oj",
"citation": "Regulation (EU) 2024/1689, Article 3(1)."
},
{
"type": "regulation",
"title": "EU AI Act Article 50 — Synthetic-content disclosure",
"url": "https://eur-lex.europa.eu/eli/reg/2024/1689/oj",
"citation": "Regulation (EU) 2024/1689, Article 50."
},
{
"type": "standard",
"title": "NIST AI 100-4 — AI Risk Management Framework",
"url": "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-4.pdf",
"citation": "NIST."
}
],
"symbol_id": "generated_content",
"variables": [
{
"id": "additional_description",
"label": [
{
"locale": "en",
"value": "Description"
}
],
"required": true
}
],
"shape": "circle",
"icon_variants": [
"default",
"dark",
"de_identified",
"de_identified.dark",
"pseudonymous",
"pseudonymous.dark",
"identifiable",
"identifiable.dark"
]
},
{
"id": "input_operational_data",
"category_id": "input_dataset",
"title": [
{
"locale": "en",
"value": "Operational data"
}
],
"description": [
{
"locale": "en",
"value": "Schedules, routes, budgets, occupancy counts, public records, or other administrative data — not about any one person. The AI takes in data describing how a place or service runs. Examples: school occupancy by hour, trash-collection routes, a transit timetable, a department budget."
}
],
"authoring_guidance": [],
"examples": [],
"sources": [],
"symbol_id": "operational_data",
"variables": [
{
"id": "additional_description",
"label": [
{
"locale": "en",
"value": "Description"
}
],
"required": true
}
],
"shape": "circle",
"icon_variants": [
"default",
"dark",
"de_identified",
"de_identified.dark",
"pseudonymous",
"pseudonymous.dark",
"identifiable",
"identifiable.dark"
]
},
{
"id": "input_physical_action",
"category_id": "input_dataset",
"title": [
{
"locale": "en",
"value": "A physical action"
}
],
"description": [
{
"locale": "en",
"value": "A signal that something in the physical world changed — a door opening, a light turning on, an alert sounding. The AI takes in evidence of these changes from upstream actuators or sensors. Examples: a turnstile-unlock event, an HVAC adjustment recorded by a sensor, a public-address alert played upstream."
}
],
"authoring_guidance": [],
"examples": [],
"sources": [
{
"type": "regulation",
"title": "EU AI Act Article 3(1) — outputs that \"influence physical or virtual environments\"",
"url": "https://eur-lex.europa.eu/eli/reg/2024/1689/oj",
"citation": "Regulation (EU) 2024/1689, Article 3(1)."
}
],
"symbol_id": "physical_action",
"variables": [
{
"id": "additional_description",
"label": [
{
"locale": "en",
"value": "Description"
}
],
"required": true
}
],
"shape": "circle",
"icon_variants": [
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"dark",
"de_identified",
"de_identified.dark",
"pseudonymous",
"pseudonymous.dark",
"identifiable",
"identifiable.dark"
]
},
{
"id": "input_recommendation",
"category_id": "input_dataset",
"title": [
{
"locale": "en",
"value": "A recommendation or prediction"
}
],
"description": [
{
"locale": "en",
"value": "A suggestion, forecast, risk score, or ranking produced earlier by another system. The AI takes in this advisory output and uses it. Distinct from a binding decision because it advises rather than determines. Examples: a forecasted demand curve, a risk score from a prior model, a recommended route from a navigation service."
}
],
"authoring_guidance": [],
"examples": [],
"sources": [
{
"type": "regulation",
"title": "EU AI Act Article 3(1) — output classes (predictions, content, recommendations, decisions)",
"url": "https://eur-lex.europa.eu/eli/reg/2024/1689/oj",
"citation": "Regulation (EU) 2024/1689, Article 3(1)."
}
],
"symbol_id": "dm_priority-ranking",
"variables": [
{
"id": "additional_description",
"label": [
{
"locale": "en",
"value": "Description"
}
],
"required": true
}
],
"shape": "circle",
"icon_variants": [
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"dark",
"de_identified",
"de_identified.dark",
"pseudonymous",
"pseudonymous.dark",
"identifiable",
"identifiable.dark"
]
},
{
"id": "input_sensitive_personal",
"category_id": "input_dataset",
"title": [
{
"locale": "en",
"value": "Sensitive personal information"
}
],
"description": [
{
"locale": "en",
"value": "Health, finances, beliefs, sexuality, immigration status, or other personal information that carries legal and social risk. The AI takes in this kind of protected data. Examples: insurance eligibility at a clinic kiosk, a benefits-application status, a self-declared pronoun."
}
],
"authoring_guidance": [],
"examples": [],
"sources": [
{
"type": "regulation",
"title": "GDPR Article 9 — Special categories of personal data",
"url": "https://eur-lex.europa.eu/eli/reg/2016/679/oj",
"citation": "Regulation (EU) 2016/679, Article 9."
},
{
"type": "other",
"title": "Apple App Store privacy labels — Sensitive Info / Health & Fitness / Financial Info",
"url": "https://developer.apple.com/app-store/app-privacy-details/",
"citation": "Apple."
}
],
"symbol_id": "sensitive_personal",
"variables": [
{
"id": "additional_description",
"label": [
{
"locale": "en",
"value": "Description"
}
],
"required": true
}
],
"shape": "circle",
"icon_variants": [
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"dark",
"de_identified",
"de_identified.dark",
"pseudonymous",
"pseudonymous.dark",
"identifiable",
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}
]