The Taxonomy

A shared vocabulary for describing technology.

Each category is a question people ask about a technology, and its elements are the answers. Choose a category, then an element, to see its icon and what it means. New to the terms? Read the concepts.

Purpose

What is the purpose of this system?

Why this system exists — the outcome it’s designed to achieve.

AccessibilityEnsures that everyone has equal access to a space or a service.

Arts & CultureEnables artistic and/or cultural expression.

Border & ImmigrationSupports immigration, asylum, or border-crossing decisions or processing — for example, border-control facial recognition, visa adjudication, asylum risk assessment.

CommerceEnables the buying and selling of goods and services.

Content ModerationFilters, ranks, or removes user-generated content based on policies — for example, comment moderation on a public-facing kiosk, image filtering, or hate-speech detection.

DiningFor providing food or meal services.

EcologySupports the measurement or monitoring of the natural environment.

Education & LearningSupports teaching, learning, assessment, or educational guidance — for example, tutoring assistants, automated grading, or library and museum recommenders.

Eligibility & Public BenefitsDetermines eligibility for, or distribution of, government benefits and essential public services — for example, welfare allocation, housing assistance, child-services risk scoring.

Employment & WorkSupports hiring, scheduling, performance evaluation, or workplace operations — for example, resume screening, shift assignment, productivity monitoring.

Energy EfficiencyReduces energy use and/or helps conserve energy.

EnforcementUsed for enforcement of rules or regulations.

EntrySupports authentication or validation in order to access a space or a service.

Financial ServicesSupports lending, insurance, fraud detection, or other financial decisions about people — for example, credit scoring, insurance underwriting, transaction-fraud flagging.

Fire & EmergencySupports services that ensure public safety and health related to emergencies.

Environmental HealthMeasures or monitors aspects of the physical environment that affect human health — air quality, water quality, radiation, noise, or workplace exposures. Distinct from Healthcare, which supports clinical care for individuals.

HealthcareSupports clinical care for individuals — for example, diagnosis assistance, treatment recommendations, patient triage, or health chatbots. Distinct from Environmental Health, which monitors the physical environment.

InformSupports general-purpose information assistance — concierge, FAQ, signage, or guidance about a service. Distinct from Wayfinding & Services, which covers spatial orientation and routing within a place.

LogisticsSupports the movements of goods or materials.

Marketing & PersonalizationTargets advertising, personalizes recommendations, or profiles audiences for marketing — for example, in-store loyalty offers, personalized signage, behavioural ad targeting.

MobilitySupports how people and materials move around.

Planning & Decision-makingSupports forecasting and policy-impact analysis at the population or system level — for example, urban planning, infrastructure planning, or measuring the impact of a policy decision. Distinct from Risk Assessment & Triage, which scores individuals.

Research & DevelopmentSupports a deployed research instrument — a system that studies a place, population, or environment as part of a research program. Distinct from a production system that delivers a service to the public.

Risk Assessment & TriageScores or prioritizes individuals or cases for further attention or action — for example, recidivism scoring, hospital triage, child-welfare risk flags. Distinct from Planning & Decision-making, which forecasts at a population or system level.

Safety & SecuritySupports preventive risk reduction in physical environments — for example, fire safety, home security, or ensuring safe passage in airports or on roads. Distinct from Enforcement (rule application) and from Fire & Emergency (incident response).

Translation & Language AccessTranslates speech, text, or signs to make a place or service accessible across languages — for example, real-time interpretation at a service counter, multilingual signage.

Waste ManagementSupports the handling and disposal of waste, including as recyclables, compost and hazardous materials.

Water EfficiencyReduces water use and/or helps conserve water.

Wayfinding & ServicesEnables navigation of a location and its amenities and services.

Accountable

Who is accountable for this system?

The organization responsible for deploying and operating this system.

Context: Role

How this organization participates in the deployment of the system.

VendorThe organization that builds, supplies, or licenses the system.

DeployerThe organization that deploys and operates the system in this place.

InstitutionThe entity that is responsible and accountable for this system

OrganizationThe entity that is responsible and accountable for this system

People affected

Who is this system used on?

The people this system is used on, or whose lives its decisions affect — including those who never chose to interact with it.

Context: Relationship

How this system touches this person or group.

Decided aboutThe system produces an output about this person that affects them directly — a score, a match, an eligibility call.

Caught incidentallySwept into the system’s data even though it isn’t making a decision about them — a face in the background of a camera aimed elsewhere.

Wider communityA place or group affected collectively, beyond the individuals the system processes — a neighbourhood under watch, a ranked workforce.

PeopleThe people this system is used on, or whose lives its decisions affect.

What it does

What is this system doing?

This system's actions, said as everyday verbs — it senses, predicts, decides, creates, moves. Naming the verbs makes it easier to reason about what the system touches and what could go wrong.

Context: Human involvement

Who completes the loop — a quick, system-level signal of how much runs without a person, shown on the verb it colors. A general characterization of the deployment, not a technical audit.

A person decidesThis system suggests or informs; a person makes the call before anything takes effect. Example: a triage tool ranks cases for a clinician who chooses which to see first.

A person oversees or carries it outThis system works out the result on its own; people supervise it, can step in, or carry out the result themselves. Example: an optimizer plans the day's collection routes, and drivers run them.

Runs on its ownThis system decides and takes effect without a person reviewing each result. Example: a road toll that reprices and charges automatically as traffic changes.

ActsActs — plans a goal into steps and carries them out in software: calling tools, booking, filing, escalating, running multi-step workflows on someone's behalf. The output is an action taken, not a piece of content returned. Examples: booking a meeting, filing a return, escalating a ticket. When the action moves something physical, that is Moves.

AnswersAnswers — responds to people's questions in conversation: a chatbot, a voice assistant, an automated phone line. It holds a back-and-forth addressed to you in the moment. Examples: a transit chatbot explaining a delay, a service line routing your request. Distinct from Creates, which produces content that stands on its own afterward.

CreatesCreates — produces new text, images, audio, video, or code that did not exist before this system ran. The output is content authored by the system, not a label or score about existing data. Examples: drafting a paragraph from a prompt, generating an image from a description, composing audio, completing code. Distinct from Answers, which responds to you in conversation.

DecidesDecides — determines an outcome that takes effect: approving or denying, pricing, allocating, flagging for action. Examples: a dynamic road toll set from traffic, an application approved or refused, a dispatch assignment. When a person makes the final call from its suggestions, that is Recommends instead.

IdentifiesIdentifies — recognizes who or what something is by matching a face, voice, fingerprint, plate, or account against known records. It turns "someone is here" into "this person is here." Examples: a face match at a boarding gate, a plate lookup at a toll gantry. Distinct from Senses, which detects without naming.

MovesMoves — acts on the physical world. The system drives motors, valves, signals, or other actuators and changes something around you. Examples: steering a robot, changing a traffic-signal phase, opening a gate, adjusting a building's ventilation, releasing a parking barrier.

PredictsPredicts — estimates what is likely: a forecast, a risk score, a probability. It says what may happen; someone or something else still decides what to do about it. Examples: a queue-length forecast, a credit-risk score, a crowding prediction for the next hour. Distinct from Decides, which makes an outcome take effect.

RecommendsRecommends — suggests options for a person to choose from: a ranking, a shortlist, a suggested route or treatment. Nothing takes effect until someone accepts the suggestion. Examples: a triage ranking for a clinician, an alternate route offered to a driver, products shown first. Distinct from Decides, where the outcome takes effect on its own.

SensesSenses — turns raw signals from cameras, microphones, or sensors into things the rest of the system can use: detections, transcriptions, readings. Includes continuous monitoring that watches for events. Examples: detecting a vehicle in a camera frame, transcribing a spoken sentence, noticing that a platform is crowded. Distinct from Identifies, which puts a name to who or what it perceives.

UnderstandsUnderstands — pulls meaning from text or speech, finds related ideas, and connects what it reads to saved context. It returns matches, intents, summaries, or links to relevant prior knowledge, not new content. Examples: matching a question to the right help article, retrieving the policy clause that applies to a case, recognizing the topic of a sentence.

Risks & Mitigation

What are the risks and how are they mitigated?

The risks this system could create and how they’re being addressed.

Loss of autonomyThe risk that this system limits a person's ability to make their own decisions, control their identity, or choose freely. This includes risks of restricted access to alternatives, deceptive design that nudges decisions toward a particular outcome, and lack of meaningful consent. Mitigations may include opt-out mechanisms, transparent recommendations, defaults that protect agency, and human review of consequential decisions.

Civil liberties harmThe risk that this system compromises fundamental human rights and civil liberties — speech, assembly, movement, due process, or freedom from arbitrary surveillance. This includes risks from mass facial recognition, predictive policing, censorship of lawful expression, and chilling effects on protest. Mitigations may include strict purpose limits, judicial or independent oversight, narrow data retention, and human-rights impact assessments before deployment.

Environmental harmThe risk that this system causes environmental degradation through energy consumption, water use, e-waste, carbon emissions, or resource extraction for hardware. This includes risks from training-time compute, always-on inference, cooling demands of data centers, and rapid hardware churn. Mitigations may include energy and water efficiency targets, transparent reporting of compute footprint, hardware-lifecycle planning, and renewable-energy procurement for data-centre operations.

Financial & business harmThe risk that this system causes financial losses to individuals or organizational damage through erroneous pricing, denied services, fraud, or market manipulation. This includes risks from automated credit decisions, dynamic-pricing inequities, AI-enabled scams, and supply-chain disruption from misuse. Mitigations may include affordability guards, fairness audits of pricing models, fraud-detection layers, and clear redress procedures.

Physical harmThe risk that this system's outputs lead to physical injury to individuals or damage to property. This includes risks from autonomous vehicles, robotic actuation, faulty navigation guidance, or critical-infrastructure errors that put bodies or property in harm's way. Mitigations may include rigorous safety testing, fail-safe defaults, geofencing of hazardous behavior, and human oversight of high-stakes physical actions.

Political & economic harmThe risk that this system manipulates political discourse, interferes with elections, concentrates market power, or damages public institutions. This includes risks of synthetic media targeting voters, opaque ad-targeting, competitive distortion from monopoly access to data and compute, and erosion of institutional trust. Mitigations may include disclosure of synthetic content, antitrust attention to AI markets, audit access for regulators, and political-ad transparency requirements.

Loss of privacyThe risk that this system captures, infers, or reveals more about people than they expect or agreed to. This includes risks of tracking people's movements over time, re-identifying data that was meant to be anonymous, linking records across systems to build profiles, and reusing data for purposes beyond the one stated. Mitigations may include collecting only what is needed, processing on the device, anonymizing at the point of capture, short retention periods, and privacy impact assessments before deployment.

Psychological harmThe risk that this system causes emotional or mental-health impairment, directly or indirectly. This includes risks from addictive feedback loops, distress caused by surveillance, harassment via generated content, and anxiety from automated denials. Mitigations may include content warnings, well-being safeguards, age-appropriate design, accessible support channels, and rate limits on engagement-maximizing behavior.

Reputational harmThe risk that this system damages the reputation of individuals, groups, or organizations through misidentification, false categorization, or stigmatizing labels. This includes risks of inaccurate face recognition, defamatory generated content, and unfair public scoring that follow people across contexts. Mitigations may include accuracy thresholds before public-facing labels are applied, human review of high-impact identifications, takedown procedures, and clear rights to correction.

Security & data breachThe risk that this system or the data it holds is accessed, stolen, altered, or taken over by someone who should not have it. This includes risks from hacked cameras or sensors, leaked databases, intercepted transmissions, insider misuse, and tampering that makes a system report or act falsely. Mitigations may include encryption in storage and in transit, strict access controls, regular security testing, prompt software updates, and notifying affected people when a breach occurs.

Societal & cultural harmThe risk that this system harms communities or culture through erosion of trust, loss of linguistic and cultural diversity, or unhealthy dependency on opaque systems. This includes risks of misinformation at scale, homogenization of cultural expression, replacement of local knowledge, and degradation of public-information ecosystems. Mitigations may include multilingual support, partnerships with affected communities, provenance signals on generated content, and investment in local-language and local-context models.

Rights

What are your rights?

What you can do in relation to this system — how to access, contest, or opt out.

Right to AccessThe right to request and receive information about what personal data a system has collected about you, how this data is being used, and what decisions have been made using this information. This includes the right to obtain a copy of your data in a readable format.

Right to Algorithmic TransparencyThe right to be informed in plain language about how a system works in general — the logic involved, the significance of the processing, and the likely consequences for the people it affects. This is the right to understand the system itself; the right to an explanation of a specific decision made about you is a separate right.

Right to Be ForgottenThe right to have your personal data deleted from a system when the data is no longer needed for its original purpose, when you withdraw consent, or when there is no legitimate interest in continuing to process it. Where technically feasible, this also includes having the system "unlearn" information derived from your data. This is a right of deletion; the right to stop processing without deletion is a separate right.

Right to ContestThe right to challenge a decision a system has made about you — to provide additional information, express your point of view, and have the decision reconsidered based on your input. This is a remedy after a decision has been made; the right to a human review before a decision is acted on is a separate right.

Right to Correct Your DataThe right to have inaccurate or incomplete data that a system holds about you corrected or completed. This is separate from the right to access your data (which is the right to read it) and the right to be forgotten (which is the right to delete it). When the original data is corrected, downstream decisions made from the faulty data should be revisited.

Right to an Explanation of a DecisionThe right to a clear and meaningful explanation of how a system contributed to a specific decision affecting you — what role the system played, the main factors that led to the outcome, and what it means for you. This right covers the decision actually made about you; the right to be informed about how the system works in general is a separate right.

Right to Non-discriminationThe right to be free from discriminatory treatment by a system based on protected characteristics such as race, gender, age, religion, disability, or sexual orientation. Organizations must implement and demonstrate appropriate technical and organizational measures to prevent discriminatory outcomes from their systems.

Right to Stop ProcessingThe right to ask an organization to stop using your personal data, even when the data itself is not deleted. This is separate from the right to be forgotten (which deletes the data) and from the right to purpose limitation (which constrains the original stated purposes). It is the user-invoked stop-now affordance — your data may stay on file, but no further processing should happen against it.

Right to Purpose LimitationThe right that your data is only used for the specific purposes that were stated when it was collected. The organization must declare and document those purposes before collection begins, and is barred from later using your data for new, unrelated purposes without your knowledge or consent. This is an upstream limit on what use is ever allowed; the right to stop processing already underway is a separate right.

Right to a Human ReviewThe right to have a person — not an automated system — make or meaningfully reconsider a decision affecting you. This is a path to a non-automated decision, separate from the right to challenge a decision that has already been made. The reviewing person must have the authority and information to actually change the outcome.

Right to Be InformedThe right to know that a system is in operation before it collects data about you or any decision affecting you is made. This includes being told when data about you is being collected, when you are interacting with an automated system rather than a person, when content is AI-generated, and when biometric or emotion-recognition technology is in use. This is the precondition for every other right in this category — you cannot exercise rights you do not know apply to you.

Collection Technology

What captures the data this system uses?

The devices and channels that capture this system's input data — cameras, sensors, microphones, forms, or feeds from other systems. The instrument that collects the data, distinct from the algorithm that processes it.

Biometric ScannerCaptures a physical trait for identification — fingerprint, palm, iris, or a face scan at a gate, kiosk, or turnstile. The capture device itself; matching the scan against stored templates is biometric recognition, which appears under Algorithm or Model.

CameraCaptures still images or video of a place — mounted on a pole, a vehicle, a drone, or built into a device. Includes infrared and thermal cameras. This is the capture device itself; the techniques that analyze the footage appear under Algorithm or Model, and how identifiable the footage is appears on the Input Data category.

Connected Device TelemetryReadings streamed automatically by connected things — vehicles reporting speed and position, wearables reporting heart rate, smart meters reporting energy use, machines reporting their own status. Continuous capture that runs in the background of everyday use.

Environmental SensorMeasures conditions in the surroundings — air quality, temperature, humidity, noise level, water level, or weather. These sensors read the environment, not people, though noise and air readings can still reflect human activity.

Existing RecordsData the organization already holds — customer accounts, case files, registries, transaction histories, past applications. Nothing new is captured from the world; the system draws on what was recorded before, sometimes for a different original purpose.

MicrophoneCaptures sound — speech, ambient noise, or specific audio events like breaking glass or gunshots. Found in smart speakers, kiosks, intercoms, and acoustic monitoring installations. The capture device itself; speech recognition and other analysis appear under Algorithm or Model.

Network ConnectionConnects this system to a network — a cellular modem, a WiFi access point, or a wired link. It moves data rather than capturing it, but connection logs can still reveal which devices were nearby or connected, and an access point that offers public WiFi is also a service people join.

Positioning & LocationDetermines where something is — GPS receivers in vehicles or apps, Bluetooth beacons, or indoor positioning systems. Produces location traces, one of the most revealing kinds of personal data when tied to a person or their device.

Presence & Motion SensorDetects that someone or something is there, or is moving, without capturing an image — passive infrared, ultrasonic, pressure mats, door counters, seat occupancy sensors. Tells the system that a space is occupied, not who occupies it.

Third-Party Data FeedData received from another organization — purchased, licensed, or delivered through an API or data-sharing agreement. Examples: credit bureau records, weather services, map providers, data brokers. The people the data describes may never have dealt with this system's operator.

User InputInformation people provide deliberately — a form they fill in, text they type, a search they run, a photo they upload, a voice command they speak. The person knows they are handing something over, unlike sensing that happens around them.

Vehicle DetectionSenses vehicles on a road or in a space — radar, LiDAR, induction loops in the pavement, piezoelectric strips, or gantry-mounted detectors. Used to count traffic, measure speed, and detect congestion. License-plate cameras belong under Camera.

Wireless Signal SensingDetects the radio signals devices give off — WiFi probes from phones, Bluetooth, RFID tags, or NFC taps. Often used to count or track devices moving through a space; a device's signal can act as a persistent identifier for the person carrying it.

Input Data

What input data does this system process?

The input data this deployed system processes when it operates.

Context: Personal Information

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.

Anonymized dataData 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.

Pseudonymous dataEach 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.

Identifiable dataThe 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.

Data about a measurementSensor readings — air quality, temperature, sound level, energy, water flow. The system 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.

Data about a placeLocations, routes, surroundings, or floor plans. The system takes in data describing where something is. Examples: GPS coordinates from a phone, foot-traffic counts on a sidewalk, a map of building corridors.

Data about behaviourTaps, choices, paths walked, dwell time, purchases, or queries. The system 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.

Biometric dataFaces, fingerprints, voice prints, gait, gestures, gaze, posture. The system 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.

A decision about youAn eligibility, classification, ranking, or yes/no that another system already made about a person. The system 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.

Generated contentText, images, audio, or video that an AI system made. The system 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.

Operational dataSchedules, routes, budgets, occupancy counts, public records, or other administrative data — not about any one person. The system 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 physical actionA signal that something in the physical world changed — a door opening, a light turning on, an alert sounding. The system 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 recommendation or predictionA suggestion, forecast, risk score, or ranking produced earlier by another system. The system 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.

Sensitive personal dataHealth, finances, beliefs, sexuality, immigration status, or other personal information that carries legal and social risk. The system takes in this kind of protected data. Examples: insurance eligibility at a clinic kiosk, a benefits-application status, a self-declared pronoun.

Algorithm or Model

What algorithm or model processes the data?

What processes the input — the algorithm family, model class, or technique used to turn inputs into outputs.

Affect & Emotion AnalysisTechniques that infer emotional state, mood, or sentiment from text, speech, facial expressions, or physiological signals. Used to gauge crowd sentiment, evaluate service experience, or screen for distress. The accuracy and cultural fairness of affect inference is contested, and some jurisdictions restrict its use in workplaces and public services.

Anomaly DetectionTechniques that flag unusual events, patterns, or values that depart from a learned baseline of normal behavior. Used for equipment-failure prediction, fraud screening, intrusion detection, water-leak monitoring, and unusual crowd-flow alerts. Outputs are typically thresholded scores rather than direct decisions.

Biometric RecognitionTechniques that identify or verify a specific person from biological signals — face, fingerprint, voice, iris, or gait. Distinct from generic computer vision because the output is tied to a named individual. Many jurisdictions restrict or prohibit biometric recognition in publicly accessible spaces.

Classification & PredictionTechniques that assign a label to an input or estimate a numeric outcome — including classifiers (spam vs. not spam, risk tier), regressors (predicted demand, expected wait), and time-series forecasters (next-hour ridership, energy load). Built from labeled historical data using statistical or machine-learning models.

Clustering & SegmentationTechniques that group people, places, or events into clusters based on similarity, without using pre-defined labels. Used to segment audiences, mobility patterns, energy-use profiles, or service-demand zones. Outputs are cluster assignments that downstream systems may treat as categories — even though clusters are inferred, not authored.

Computer VisionTechniques that interpret images or video — counting people, detecting objects, reading text in pictures, tracking motion, or segmenting scenes. Common in occupancy sensing, traffic and crowd monitoring, accessibility assistance, and signage inspection. Distinct from biometric recognition, which identifies specific individuals.

Language ModelsModels that read, write, summarize, translate, or hold a conversation in human language. Includes large language models (LLMs), chatbots, and generative-text systems. They work by predicting the most likely next tokens given the input and any prior context.

OptimizationTechniques that search for the best assignment, route, schedule, or allocation under constraints. Includes vehicle routing, transit signal timing, energy and HVAC scheduling, staff rostering, and resource allocation. Works by exploring possibilities to maximize or minimize a stated objective such as cost, time, or emissions.

Privacy-Preserving TransformationTechniques whose primary job is to transform input data so identifying details are removed, blurred, or replaced before any downstream use. Includes face blurring, license-plate masking, tokenization, k-anonymization, differential privacy, and synthetic-data generation. Acts as a barrier between raw collected data and the data used for analysis or decisions.

Recommendation & RankingTechniques that suggest or order items for a person — services, listings, news, wayfinding options — based on preferences, behavior, or similarity to other people. Includes collaborative filtering, content-based recommenders, and learned ranking. Personalizes what each person sees from the same underlying catalog.

Search & RetrievalTechniques that find and surface relevant content from a body of documents, records, or media in response to a query. Includes semantic search, vector retrieval, and retrieval-augmented generation (RAG) pipelines that feed relevant passages into a language model. Used in document QA, public-records lookup, and library or archive search.

Speech & AudioTechniques that convert between spoken words and other formats, or that classify non-speech sound. Includes speech-to-text (transcription), text-to-speech (synthetic voice), keyword spotting, and audio event detection. Voice-as-identity (recognizing who is speaking) belongs in Biometric Recognition.

Output Data

What does this system produce?

The output data this deployed system produces when it operates — decisions, content, and actions.

Context: Personal Information

How identifiable the data is when this system produces it. The element name says what the output is about; this colour says how directly it identifies a person.

Anonymized dataOutput about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so the result can’t reasonably be tied back to an individual.

Pseudonymous dataThe output is labelled with a token (hash, ID, template) that lets this system tie the result to the same person across events without revealing a name. Reidentification is possible with extra information.

Identifiable dataThe output names a person directly (name, address, account, recognisable face or voice, plate) or carries a token this system resolves to legal identity in the same step.

Data about a measurementThe system produces numeric measurements over time — sensor outputs, derived quality scores, forecasted values. Examples: an hourly air-quality score for a plaza, a predicted ridership curve for tomorrow, an aggregated noise-level reading published to a public dashboard.

Data about a placeThe system produces location, route, region, or floor-plan output. Examples: a wayfinding route shown on a kiosk, a heatmap of foot-traffic density, a geofence boundary published to downstream systems.

Data about behaviourThe system produces records of what people did — paths walked, items selected, dwell times, interaction patterns. Examples: a weekly report of most-visited destinations, a session log of touchscreen interactions, an inferred route someone took through a venue.

Biometric dataThe system produces output containing biological signals from a person's body — face templates, voice embeddings, gait vectors, gesture sequences. Examples: an enrolled face template stored downstream, a voice embedding for later matching, a gait signature exported to another system.

A decision about youThe system produces a binding determination about a person — eligibility, classification, ranking, or yes/no. Examples: an access-control system deciding to open a gate, an automated benefits eligibility result, an automated screening that rules someone out.

Generated contentThe system produces new content — text, images, audio, or video that did not exist before. Examples: an LLM-written service advisory shown on a sign, a synthesized voice announcement played over a public-address system, a generated illustration on a digital kiosk.

Operational dataThe system produces administrative output — schedules, routes, occupancy estimates, budget allocations, or other records about how a place or service runs. Not about any one person. Examples: an optimized trash-collection route, a school occupancy projection, a recommended budget allocation for next quarter.

A physical actionThe system triggers something in the physical world — a door unlocking, a light turning on, an alert sounding, signage changing, HVAC adjusting. Examples: a turnstile that opens on facial match, a public-address alert played automatically, an HVAC adjustment commanded by a building model.

A recommendation or predictionThe system produces an advisory output — a suggestion, forecast, risk score, or ranking. Distinct from a binding decision because it advises rather than determines. Examples: a signage system suggesting an alternate route based on predicted crowding, a forecasted demand curve for tomorrow, a recommended next destination on a kiosk.

Sensitive personal dataThe system produces output about health, finances, beliefs, sexuality, immigration status, or other categories that carry legal and social risk. Examples: a triage priority assigned at a clinic, an eligibility result sent to a benefits portal, an inferred protected attribute exported downstream.

Manifestation

How do people encounter what this system produces?

The form this system's output takes when it reaches the world — a screen or sign, a price, a notification, a gate opening, a spoken reply. How people meet the output, distinct from the output data itself.