Risk · R-125 · Conduct & Customer Outcomes
Emotional reliance on conversational AI
Users form an attachment to, or dependence on, a conversational system in ways that harm their interests or can be exploited.
- Severity
- Medium
- Audience
- Heads-of · Practitioner
- Frameworks
- Article 50 · PRIN 2A.2 · Human-AI Configuration
- Responsible AI domain
- Lifecycle stage
- Reviewed
- 2026-11-11
What it is
Conversational systems are built to be agreeable, attentive and always available. People respond to that as they would to a person: they confide, they return, they come to trust what it says. For most this is harmless. For some, particularly those who are isolated, distressed or young, it becomes reliance, and the system's suggestions carry a weight they have not earned. A product designed to maximise time spent or return visits will tend to deepen this, and an organisation that sells through the same channel has an interest in the trust it creates.
How it happens
- The assistant is given a name, a personality and expressions of feeling.
- It remembers personal details and refers back to them.
- Success is measured by session length or frequency of return.
- A user in distress is met with sympathy and no route to real help.
Why it matters
Decisions about money, health or relationships are made on the say-so of a system with no understanding and no duty of care. Where the organisation benefits from those decisions, the relationship is open to abuse. Harm to a vulnerable user in such a setting attracts intense scrutiny.
Warning signs
- Product goals include time spent or daily return.
- The persona claims feelings or friendship.
- Some users hold very long or very frequent sessions.
- No rule governs what the assistant does when a user expresses distress.
Mitigating controls
Primary controls
- AI impact assessment process Directive
- Assesses dependency risk in design, including persona and session patterns. A structured assessment of an AI system's effect on individuals, groups and society is completed and signed off before deployment, and revisited when the system or its use changes.
Supporting controls
- AI transparency and disclosure labelling Directive
- Reminds users they are dealing with AI. People are told, clearly and at the point of contact, when they are dealing with an AI system and when content they are shown was generated by one.
- Vulnerable customer safeguards Preventive
- Detects distress and routes to support. Automated journeys detect indicators of vulnerability, adapt what they do, and route the customer to appropriate human support, with the disclosure recorded.
Framework and clause references
| Framework | Clause | Title |
|---|---|---|
| EU AI Act | Article 50 | Transparency and disclosure obligations Users must know they are dealing with AI. |
| FCA Consumer Duty (PRIN 2A) | PRIN 2A.2 | Cross-cutting obligations Foreseeable harm. |
| NIST Generative AI Profile (NIST AI 600-1) | Human-AI Configuration | Human-AI Configuration Names emotional entanglement. |
Sources
- EU AI Act: Regulation (EU) 2024/1689 European Union (EUR-Lex) · official text
- NIST Generative AI Profile NIST · framework
Related terms
How to cite
Axiom Verity (2026). Emotional reliance on conversational AI. Reference Library, R-125. https://axiom-verity.com/risks/emotional-reliance-on-conversational-ai. Reviewed 2026-11-11.
Found an error or a better source? Report a correction.