How to Make AI More Transparent for Customers and Users
If your business uses AI, people should understand when it is being used, what it is doing, and what to do if something goes wrong.
AI transparency is about making your use of AI clear enough that customers, users and internal teams can understand how the system fits into your product or service. It does not mean giving away source code or trade secrets. It means being open about how AI is used, what its limits are, and how decisions are made.
Why transparency matters
AI can improve speed and efficiency, but it can also create risk.
If users do not understand how AI is being used, they may:
rely too heavily on outputs;
misunderstand what the tool can and cannot do;
lose trust if something goes wrong; or
raise complaints when decisions cannot be explained.
Good transparency helps:
build customer trust;
reduce complaints and disputes;
support privacy and compliance obligations; and
make it easier to identify and fix issues early.
1. Tell users when AI is being used
Start with the basics: tell people when AI is part of the product, service or workflow.
This could include:
AI-generated customer support responses;
automated document reviews;
AI-based recommendations; or
AI used in internal decision-making that affects users.
Example:
If your SaaS platform uses AI to draft first responses to support tickets, tell users in the chat interface that responses may be AI-assisted and reviewed by a human where needed.
A simple notification can go a long way. Users should not have to guess whether they are dealing with AI.
2. Explain what the AI is used for
Be clear about:
what the AI is designed to do;
what data it uses;
what factors influence outputs; and
what the system is not designed to do.
Example:
A SaaS platform using AI to prioritise customer support tickets could say:
“Our AI tool helps sort support requests based on urgency and issue type so our team can respond faster. Final decisions on escalations and resolutions are made by our support team.”
This helps users understand what the AI is doing, what it is not doing, and reduces the risk of customers assuming the system is making final decisions on its own.
3. Keep good records throughout the AI lifecycle
Transparency is mostly process-driven.
Businesses should keep clear records of:
system purpose;
design decisions;
data sources;
limitations;
performance issues; and
changes made over time.
Example:
If your product team changes the AI model used for customer recommendations, record:
what changed;
why it changed;
what testing was done; and
whether customer outcomes were affected.
This helps explain how the AI works in practice and makes it easier to respond to customer questions or complaints.
4. Give users a way to ask questions or challenge outcomes
Users should have a practical way to:
ask how AI was used;
raise concerns; or
request human review where appropriate.
This is especially important if AI affects:
access to services;
pricing;
recommendations; or
other decisions that matter to the user.
Example:
If an AI system flags a customer account as suspicious and limits access, the customer should be able to contact support and request a manual review.
A simple escalation process can reduce risk and improve trust.
5. Make sure people in your business understand the system
Transparency is not just customer-facing.
Your team should understand:
what the AI does;
where it can fail;
what its limits are; and
when human intervention is needed.
Businesses should build internal capability so relevant staff can:
interpret technical documentation;
answer user questions; and
identify issues early.
Example:
If your sales team promises AI features to customers, they should understand:
what the tool actually does;
what it cannot guarantee; and
how customer data is handled.
6. Use tools that help monitor AI performance
AI systems can be hard to explain, especially where the model is a black box.
While technology alone will not solve transparency, businesses can improve visibility by using:
dashboards;
audit logs;
performance monitoring tools; and
controls that flag unusual outputs.
Example:
If your AI tool generates reports for clients, use an audit log to track:
what inputs were used;
when outputs were generated; and
whether a user edited the result.
These tools help track issues and support accountability.
Questions?
Reach out to us at hello@pixellegal.com.au - we can provide guidance on how to make AI more transparent for users and customers.
Disclaimer
This article is provided for general information purposes only and does not constitute legal advice. It does not take into account your organisation’s specific circumstances, systems, or regulatory obligations. You should obtain tailored legal advice before taking action.
