AI Ethics at Work
Everyday AI use is full of small ethical choices: what you paste in, what you forward, and what you let a tool decide. This course covers bias, transparency, attribution, privacy, and fairness in plain, practical terms. You'll learn five questions to ask before acting on any AI output, and the lines a professional never crosses. You'll also practice how to raise a concern and handle pressure to cut corners.
Course details
Duration
57m 43s
Lectures
15
Video
1 hour
Quizzes
1
Level
Beginner
Course Description
AI has moved ethical decisions out of the boardroom and into everyday tasks. Forwarding an unverified claim, pasting a colleague's data, or letting a score decide about a person are all ethical choices. This course gives you a simple, repeatable way to make them well.
What you'll learn
- The five ethical questions to ask before acting on an AI output
- How bias shows up in everyday output, and your duty to verify before sharing
- Privacy as an ethical line, and the minimize-and-anonymize habit
- When to disclose AI involvement, and how to handle attribution and ownership
- Why AI should inform decisions about people, never make them
- How to raise a concern and respond to pressure to cut corners
Who it's for
Every employee who uses AI tools at work, plus managers who set the tone for how their teams use them.
15 short video lessons in three modules: Ethics Is a Daily Question; Honesty, Ownership, and Fairness; and Doing the Right Thing, Practically.
Module 1: Ethics Is a Daily Question
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1.1 Small Choices, Real ConsequencesThree ordinary AI moments with ethical weight — why ethics at work is a daily question, not a policy document.
In this lesson:
- Recognize ethical weight in ordinary AI-assisted tasks
- Explain why AI moves ethical choices into everyday hands
- Adopt the habit of pausing before acting on an output
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1.2 The Five Questions
Is it fair? Is it true? Is it private? Is it honest about its source? Who is accountable? — the checklist for any AI output.
In this lesson:
- State the five ethical questions from memory
- Apply them to an AI output in under a minute
- Recognize which question each later lesson expands
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1.3 Fairness and Bias in Output
Where the tilt comes from, what it looks like in ordinary output, and reading for what's missing.
In this lesson:
- Explain where bias in AI output comes from
- Recognize the three common shapes of bias in everyday output
- Read an output for who's missing, not just what's wrong
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1.4 Truth and the Duty to Verify
Passing on an unchecked claim is a choice, not an accident — the professional duty AI makes newly urgent.
In this lesson:
- Explain why forwarding an unverified AI claim is an ethical act
- Apply a proportionate verification standard
- Handle the moment you can't verify
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1.5 Privacy as an Ethical Line
Other people's data in your prompts — why it's an ethical question before it's a compliance one, and the habit that keeps you on the right side.
In this lesson:
- Explain why privacy in prompts is an ethical duty, not only a policy
- Recognize other people's data before it enters a tool
- Apply the minimize-and-anonymize habit
Module 2: Honesty, Ownership, and Fairness
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2.1 Disclosure: When to Say AI Helped
The audiences that expect disclosure, the ones who assume it, and a simple rule for the space between.
In this lesson:
- Decide when AI involvement should be disclosed
- Distinguish audiences who expect disclosure from those who assume it
- Phrase a disclosure naturally
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2.2 Attribution and Ownership
Using others' work as input, crediting sources, and who owns what comes out — the ownership questions in everyday AI use.
In this lesson:
- Credit sources when AI reshapes others' work
- Recognize input that shouldn't be fed to a tool without permission
- State who owns AI-assisted output at work and what that means
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2.3 AI and Decisions About People
Hiring, performance, customers — why AI can inform a decision about a person but must never make it, and how to keep that line.
In this lesson:
- State the inform-never-decide principle and why it exists
- Recognize when an AI output has quietly become the decision
- Keep a human decision genuinely human
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2.4 Fair Treatment of Customers and Colleagues
Personalization vs. manipulation — the line between using AI to serve people better and using it to work them.
In this lesson:
- Distinguish personalization from manipulation
- Apply the line test to AI-assisted messaging and offers
- Treat colleagues fairly when AI touches internal communication
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2.5 Accuracy in Public-Facing Work
Marketing, claims, and the standard that doesn't change because a machine drafted it.
In this lesson:
- Apply the public-facing accuracy standard to AI-drafted content
- Recognize the claim types AI tends to inflate
- Run the pre-publication check
Module 3: Doing the Right Thing, Practically
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3.1 Company Policy as the Floor, Not the Ceiling
Reading the AI policy for what it requires, and the judgment it expects from you where it's silent.
In this lesson:
- Read an AI use policy for its requirements and its gaps
- Explain why policy is a minimum, not a complete answer
- Apply the five questions where policy is silent
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3.2 When Something Feels Wrong
Recognizing the signal, naming what it's pointing at, and deciding what it needs — before it becomes a problem.
In this lesson:
- Recognize the early signals of an ethical problem with AI use
- Name the concern precisely using the five questions
- Decide whether a signal needs a pause, a fix, or a conversation
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3.3 How to Raise a Concern
Who to tell, what to say, and why early is better — raising an AI ethics concern so it gets acted on.
In this lesson:
- Choose the right person to raise a concern with
- Frame a concern so it's specific, actionable, and non-accusatory
- Explain why raising it early protects everyone, including you
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3.4 Handling Pressure to Cut Corners
The fast answer vs. the right answer — recognizing pressure, and the scripts that hold the line without a fight.
In this lesson:
- Recognize the forms pressure to cut corners takes
- Respond with scripts that hold the standard and offer a path
- Distinguish a reasonable shortcut from a corner that can't be cut
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3.5 The Ethical Professional
The five questions as a habit, the lines a professional doesn't cross, and the reputation it builds — the finale.
In this lesson:
- Recall the five questions and the lines never crossed
- Describe how the habit builds a professional reputation
- Complete AI Ethics at Work
Learn at your own pace
Study the materials at your convenience, you can always return to them to repeat
Certificate of completion
Complete every lesson and pass the final exam to earn a RiseCert certificate of completion you can download and share.
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Got Questions? We've Got Answers!
Answers to the most common questions about this course.
What is the AI Ethics at Work course?
AI Ethics at Work is a RiseCert online course that treats AI ethics as a series of small, everyday choices: what you paste into a tool, what you forward, and what you let a tool decide. It covers bias, verification, privacy, disclosure, attribution, and fairness in plain terms, across 15 video lessons (about an hour) and a final exam.
Who should take an AI ethics course for the workplace?
AI Ethics at Work is for every employee who uses AI tools on the job, in any role or industry, plus managers who set the tone for how their teams use them. It is especially useful for people who handle customer or colleague data, make or influence decisions about people, or publish content under their organization's name.
Do I need a background in ethics, law, or AI to take the course?
No. AI Ethics at Work assumes no background in philosophy, law, or technology. It avoids abstract theory and instead works through ordinary situations, such as a complaint email, a ranked list of job applicants, or a marketing statistic, using five plain questions anyone can apply in under a minute.
What are the five ethical questions to ask before using AI output?
AI Ethics at Work teaches five questions to run on any AI output or prompt: Is it fair? Is it true? Is it private? Is it honest about its source? Who is accountable? They take under a minute, with a deeper look only where one raises a flag, and every lesson in the course expands one of them.
What will I be able to do after completing the course?
You'll be able to apply the five questions in under a minute, read AI output for bias and who's missing, verify claims in proportion to their reach, minimize and anonymize data before prompting, decide when to disclose AI help, keep decisions about people human, raise a concern constructively, and answer pressure to cut corners with practical scripts.
What topics and modules are covered?
The course has three modules. Ethics Is a Daily Question covers everyday choices, the five questions, bias, verification, and privacy. Honesty, Ownership, and Fairness covers disclosure, attribution and ownership, AI in decisions about people, fair treatment, and public accuracy. Doing the Right Thing, Practically covers company policy, warning signals, raising concerns, pressure, and professional habits.
Should I tell people when I've used AI at work?
Sometimes. AI Ethics at Work teaches that evaluators, decision-makers, the public reading authoritative content, and people in personal exchanges generally expect disclosure, while routine internal drafts usually don't need it. For the gray area, ask whether the person would feel misled about effort, expertise, or sincerity if they found out later.
How does bias show up in everyday AI output?
In AI Ethics at Work, bias usually appears as a quiet tilt rather than an obvious error, in three shapes: assumption, where the tool fills in details nobody gave it; omission, where a group or perspective is missing; and default, where one kind of person is treated as the norm. The course teaches reading for who's missing.
Can AI be used in hiring and other decisions about people?
AI Ethics at Work teaches that AI should inform decisions about people and never make them. Tools can summarize and sort, but a person should review the whole set, decide on stated criteria, and be able to explain each outcome. The course shows how rankings, flags, and scores quietly become the decision when no one reviews.
How is this different from our company's AI use policy training?
Policy training covers the rules your organization has written. AI Ethics at Work treats that policy as the floor, not the ceiling: it shows how to read a policy for its silences and apply the five questions where the rules say nothing. It also covers raising concerns and handling pressure, which policies rarely address.
How long does the course take?
AI Ethics at Work has about an hour of video across 15 lessons that run a few minutes each, plus a final exam. The lessons are organized into three modules: Ethics Is a Daily Question; Honesty, Ownership, and Fairness; and Doing the Right Thing, Practically. Most learners complete it in one or two sittings.
Is the course self-paced and fully online?
Yes. AI Ethics at Work is 100% online and self-paced, and your access does not expire. You can start, pause, and resume whenever you like on a computer, tablet, or phone with a modern web browser, and there is nothing to install.
Is there a final exam?
Yes. AI Ethics at Work ends with a 30-question multiple-choice final exam covering all three modules. You need a score of 80% or higher, which is 24 of 30 correct, to pass, and you can retake it as many times as you need. The questions are workplace scenarios that test how you apply the course.
Will I receive a certificate for completing AI Ethics at Work?
Yes. After you complete all 15 lessons and pass the final exam, you can download a RiseCert certificate of completion. It is a course completion certificate, not a license, a professional certification, or a continuing education certificate, and it documents that you finished the course and passed its exam.
Does the course qualify for CE, CPE, or CEU credit?
No. AI Ethics at Work is not currently approved or accredited for continuing education credit (CE, CPE, or CEU) by any state, licensing body, or professional association. If you need ethics credit hours for a license or designation, take a course approved by the body that governs your credential.
Is the content up to date with how AI is used at work today?
Yes. AI Ethics at Work is built on principles that hold as tools change, such as the five questions, proportionate verification, and minimize-and-anonymize, rather than on any single product. It also notes that many jurisdictions now require human review of automated decisions about people, while keeping the focus on everyday judgment.
Is AI Ethics at Work legal or compliance advice?
No. The course is educational and is not legal, HR, or compliance advice. It teaches ethical habits for everyday AI use, but your organization's policies, your legal and privacy teams, and applicable law govern your specific situation. For questions about data, ownership, or regulated claims, the course itself recommends asking the function that owns them.
How much does the course cost, and can I buy it for my team?
AI Ethics at Work costs $99 per learner as a one-time purchase, with no subscription or recurring fees. To enroll a team, department, or whole organization, contact RiseCert through the Contact Us page at risecert.org and we will help arrange access for your group.
Can I get a refund?
No. All sales are final and no refunds are issued, as stated in the RiseCert Terms of Use. Please review the course description, lesson list, and these FAQs before purchasing, and reach out through the Contact Us page at risecert.org if you have any questions first.
What does 'minimize and anonymize' mean for AI prompts?
Minimize and anonymize is the privacy habit taught in AI Ethics at Work. Minimize means giving the tool only what the task needs, such as the issue and the tone, not account numbers or addresses. Anonymize means replacing names and sensitive details with neutral descriptions, like "a long-standing customer," so the draft is just as good without exposing anyone.