Check AI Output and Say When AI Helped

The last risk comes at the end of the task. You have an AI draft. It reads well. It is tempting to send it. But a clean, confident draft can still contain a wrong number, a confidential detail, copied text, or an unfair statement. Once it reaches a client or your boss, it is your work.

This lesson gives you a simple four-part check for any AI output, plus clear guidance on when to tell people that AI helped.

What You'll Learn

• The four checks before AI output leaves your desk: accuracy, confidentiality, copyright, and bias
• How to match the depth of your check to the stakes
• When and how to disclose AI use at work
• How to phrase a disclosure in one plain sentence

The four checks

1. Accuracy

AI can produce text that sounds right and is wrong. It can make up figures, dates, quotes, policy names, and sources. It can also leave out an exception that changes the meaning.

• Check every number, date, and name against the original source.
• Open any link or source it cites. Make sure it exists and says what the AI claims.
• Compare summaries with the original. Look for missing exceptions, conditions, or "unless" clauses.
• Be extra careful with policy, legal, financial, and medical claims. Those need a source you trust, not the AI's memory.

2. Confidentiality

AI output can carry data where it should not go. This happens more than people expect.

• Did details from one client end up in another client's draft? This can happen when you reuse a chat or a template.
• Does the output repeat internal figures, names, or plans that the reader should not see?
• Is the audience right for the data level? An internal number in an email to a client is a leak, even if the AI wrote it.

3. Copyright and originality

AI may produce text, code, or images that are close to existing work. Your company is responsible for what it publishes.

• Be careful with long passages that read like a published article, or code that looks copied from a known project.
• Do not ask AI to copy a competitor's materials, a paid report, or someone's style closely.
• Follow your company's rules on AI-generated images and code. Some companies limit where these can be used.
• When it matters, rewrite in your own words and add your own facts and examples.

4. Bias and fairness

AI can repeat unfair patterns from its training data. This matters most when the output is about people.

• Watch for stereotypes in descriptions of customers, staff, or groups.
• Be careful with anything about hiring, performance, pay, or access to services. Many policies limit AI use here, and some laws do too.
• Ask: would this read as fair to the person it describes?

For a deeper look at bias testing and fairness, see AI Ethics & Responsible AI.

Match the check to the stakes

Not every output needs the same effort. A rough guide:

When to say AI helped

Disclosure means telling people that AI played a part in the work. Rules vary by company, client, and industry, so start with your policy. Beyond that, these are good signs that you should disclose:

• Your policy or client contract requires it. This always comes first.
• The reader would assume a person wrote or checked it and would feel misled if they learned otherwise.
• AI did a large part of the work, not only spelling or formatting.
• The output supports a decision about money, people, or legal matters.
• The content is published under the company's name.

You usually do not need to disclose small help, like fixing grammar or suggesting a subject line, unless your policy says so. When in doubt, disclose. It costs little and protects trust.

How to disclose

Keep it short and plain. One sentence is enough. For example:

• "I used our approved AI tool to draft this summary, and I checked all figures against the source report."
• "The first draft of this section was AI-assisted and reviewed by me."
• "AI helped translate this. A fluent speaker has not reviewed it yet."

A good disclosure says what AI did and what you checked. That second part is what builds trust.

For more on disclosure norms at school and at work, see the lesson Disclosing AI Use at School and at Work.

Key Takeaways

• Before AI output leaves your desk, check accuracy, confidentiality, copyright, and bias.
• Verify every number, date, name, and source against the original.
• Look for data leaking between clients or to the wrong audience.
• Match the depth of your check to who will rely on the output.
• Disclose when policy or contracts require it, when readers would assume a person did it, or when AI did a large part of the work.
• A good disclosure says what AI did and what you checked.