Concern 932 · Working draft · editorial review in progress
Customer-trust erosion
Customer trust can erode when people believe an organization's AI use is unreliable, hidden or inconsistent with the relationship they expected.
Understanding the concern
People may welcome AI for routine administration yet object to its use in a sensitive conversation or important decision. A failure can expose that mismatch, especially if the organization cannot explain the use or offer help. Trust is therefore about purpose and treatment as well as technical accuracy.
CAF research on public attitudes to charity AI use shows that opinions vary with the proposed use. Stated attitudes are not proof of actual behavior after an incident. Think of a familiar service changing how it handles personal information without telling anyone. Clear explanations, appropriate choices and a credible response to problems can give people a basis for judging whether trust is deserved.
An example
A customer learns after a sensitive interaction that a service used AI in a way they had not expected.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
Evidence and its limits
The survey concerns attitudes to charity uses, not measured trust loss across all businesses. The incident scenario illustrates a possible mechanism rather than a reported finding.
Charities Aid Foundation · Survey research
What the public think of charities using AI ↗ (opens in a new tab)Public attitudes vary by proposed charity use. Stated views are not measured donation behavior or proof of actual trust loss after an incident.
OHCHR B-Tech Project · Human rights framework
Access to remedy and the technology sector: basic concepts and principles ↗ (opens in a new tab)Explains access to remedy in the technology sector under the UN Guiding Principles. Normative framework, not a globally uniform cause of action.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
- NIST AI RMF (opens in a new tab)
framework
- ISO/IEC 42001 (opens in a new tab)
standard
A question to discuss
What did people reasonably expect from this relationship, and how can the organization address the gap?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Trustworthiness
Trustworthiness means giving people good reasons to rely on you. It grows when promises, actions, and evidence line up. With AI, a friendly voice or a famous brand is not enough: people need to know what was checked and what could still go wrong.
In practice, keep promises small enough to test, record mistakes, and tell people when the tool's limits affect them. If a service promises human review, make sure a person really has time and authority to review it.
Ask together: What would give the people affected a sound reason to trust this use?
Stewardship
Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.
Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.
Ask together: What are we responsible for caring for over time?
Prudence
Prudence means using careful judgment before and during action. It balances worthwhile opportunities with the need to avoid harm that could have been anticipated.
Start with a limited trial, set stopping points, and prefer choices that can be undone when uncertainty is high. Review what happens rather than assuming the original plan remains sound.
Ask together: What is a careful next step, and when should we stop or change course?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
About the register and scoring →More ways to explore
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