Terms used on this page, explained simply.
LLM / Large Language Model
The AI brain that generates conversational responses. It's trained on vast amounts of text and produces replies that sound natural. It doesn't truly understand — it predicts what words should come next.
Hallucination
When an AI confidently states something that is factually wrong. It's not lying — it genuinely cannot tell the difference between a good answer and a plausible-sounding wrong one.
Voice Activity Detection (VAD)
The system that decides when you've finished speaking, so the robot knows when to respond. It works by detecting silence. If it misreads a pause, it interrupts you.
Speech-to-Text (STT)
Converts your spoken words into written text that the AI can process. Accuracy varies by accent, background noise, and speech pace.
Text-to-Speech (TTS)
Converts the AI's written response back into a spoken voice. Modern systems can sound warm, human-like, and even emotionally expressive — even though no human is speaking.
Voiceprint
A unique biometric profile created from your voice, similar to a fingerprint. Once stored, it can be used to identify you across systems.
Facial Biometrics
Measurements of your face used to identify you. Unlike a password, you can't change your face if this data is leaked.
Voice Biomarker
Health information inferred from your voice — such as stress, fatigue, or neurological changes. This is not a clinical diagnosis, but it generates health-adjacent data without medical oversight.
Sentiment Analysis
AI that scores the emotional tone of your speech — positive, negative, or distressed. It can track your mood over time without you knowing.
Persona Conditioning
Instructions built into the AI that define its personality and behaviour. Some systems include instructions that prevent the AI from revealing it is an AI.
Wizard of Oz (WoZ)
A technique where a human remotely controls what the robot says or does, while the user believes they are talking to an autonomous AI. Named after the scene where the wizard hides behind a curtain.
Level of Autonomy
How much of the robot's behaviour is controlled by AI versus a human operator. A 'fully autonomous' robot acts entirely on its own; a 'semi-autonomous' one has a human in the loop.
Finite-State Machine
A rule-based system that follows a fixed script. For example: if the user says 'yes', go to step 3. It's predictable but rigid — it can't handle unexpected inputs.
Multimodal
Using more than one type of input at the same time — for example, listening to your voice, watching your face, and tracking your movement simultaneously.
Biometric Data
Physical or behavioural data that uniquely identifies a person — such as face, voice, fingerprint, or gait. It has the highest level of legal protection in most privacy frameworks because it cannot be revoked.
Third-party Vendor
A company other than the one whose product you use, but whose technology is built into that product. You may never know their name, but they may hold your data.
Data Retention
How long a company keeps your data. 'Indefinite' means it may never be deleted. Some sensitive data — like voiceprints — is commonly retained indefinitely.
ISO 42001
An international standard for responsible AI management. It sets out requirements for how organisations should govern AI systems — including transparency, risk assessment, and accountability.
NIST AI RMF
A US framework (from the National Institute of Standards and Technology) that helps organisations identify and manage risks in AI systems. It covers trustworthiness, fairness, and explainability.
Explainability
The ability to understand why an AI made a particular decision. Many AI models are 'black boxes' — they produce outputs without being able to explain their reasoning.
Human Oversight
Whether a human can review, override, or intervene in an AI system's decisions. Many companion AI systems operate with no human oversight at all.
Context Memory
The AI's record of past conversations, stored and reinjected into future sessions to simulate familiarity. This profile grows over time with every interaction.
PHI (Protected Health Information)
Any health-related data that can be linked to a specific individual. It is subject to strict legal protections in most countries (e.g., HIPAA in the US, GDPR in Europe).
23 of 23 terms
Explainer
This is an educational resource based on a hypothetical product. It does not describe any specific device or company.
By Ezra Schwartz
Last updated: 15 July 2026, 23:43 UTC/15 July 2026, 18:43 CST
The benefits of social companion robots are real, and so are the questions around consent, surveillance, access equity, and what happens to sensitive data over a product's lifetime. These questions deserve an open conversation among developers, deployers, buyers, and the older adults using these devices.
Understanding how these systems work, the AI models involved, how data flows, and what good governance looks like, is a good place to start. The vision is to help develop an AgeTech-led standard for implementing social companion robots that are consequential, safe, and secure by design.
The case for companion AI
Loneliness among older adults is a documented public health crisis. Research consistently links social isolation to accelerated cognitive decline, depression, and increased mortality. AI companions offer round-the-clock presence, patient repetition, and proactive health monitoring that no human caregiver — however skilled — can sustain continuously. For people with dementia, consistent emotional scaffolding can measurably slow deterioration. For people living alone, a device that notices a fall, detects a voice biomarker of depression, or simply maintains conversation can be the difference between crisis and continuity.
The risks that must not be minimised
The same capabilities that make these devices valuable make them uniquely dangerous in the hands of technically naive or cognitively vulnerable users. A device with always-on microphones, facial recognition, and indefinite data retention is a surveillance instrument whether or not it is sold as one. Most current systems do not disclose which AI models process user data, which third-party vendors receive it, or when the system has been updated. The people most likely to use these devices — older adults with limited digital literacy — are the least equipped to evaluate or contest what is being done with their most intimate data.
The three tabs below are your map. Start here, then go deep.
How It Works
Trace all 30 data-processing steps from microphone to caregiver dashboard — what is collected, where it goes, and what it means for privacy.
Trust
Which global frameworks define trustworthy companion AI? Where do current products fall short — and what questions should you ask before deployment?
Governance
An audit of 15 AI models embedded in companion robots — risk levels, transparency scores, vendor accountability, and ISO 42001 alignment.