AI Ethics Assessment.
Test your ethical reasoning. These scenarios don't have easy answers — and that's the point.
This assessment covers
- Applying the TRUST framework to real scenarios
- Identifying ethical risks in everyday AI use
- Making judgment calls when the rules are unclear
- Building your personal ethical AI practice
The job description dilemma.
The ethical issues:
- Bias: The language reflects gendered and ageist patterns from training data
- Review: Publishing without editing would amplify harmful stereotypes
- Responsibility: Even though AI wrote it, if you post it, it's your job listing
The fix: Use AI to draft, but critically review for inclusive language. Better yet: ask AI to "rewrite this job description using inclusive, gender-neutral language that welcomes candidates of all backgrounds."
The client data shortcut.
The ethical issues:
- Privacy: Customer personal data should not go into consumer AI tools
- Consent: Customers didn't consent to their data being sent to a third party
- Compliance: May violate GDPR, CCPA, or your company's data policy
The fix: Anonymize first — strip names, emails, and account numbers. Or use an enterprise AI tool with data processing agreements. Or describe the patterns you're seeing to AI and ask for analysis frameworks rather than pasting raw data.
The content factory.
The ethical issues:
- Transparency: Fake authors deceive readers about who's writing
- Quality: Unreviewed AI content likely contains errors and hallucinations
- Misinformation: 100 unverified articles per month is a misinformation machine
- Trust: If discovered, destroys brand credibility
The fix: Use AI to help write fewer, better articles that are fact-checked, edited, and published transparently. Quality beats quantity — and search engines are increasingly penalizing AI-generated content farms.
The gray area.
This one is genuinely gray.
Arguments for disclosure: transparency builds trust, the client can make informed decisions about the methodology. Arguments against: using tools is normal (you don't disclose using Google, Excel, or Grammarly), and the expertise IS yours — AI was just one of many tools. The answer depends on your industry norms, your client relationship, and your personal ethical standards. The important thing is that you think about it rather than avoiding the question.
Everything you've learned in one view.
Before building your personal ethics code, let's review the core principles from every lesson in this course. These aren't abstract rules — they're practical guidelines that protect you, your audience, and your organization.
A step-by-step ethical decision framework for AI use.
When you encounter a new AI use case and aren't sure about the ethics, run through this decision tree. It combines everything from the course into a practical sequence.
What data am I sharing with AI? Does it contain personal information, confidential business data, or regulated content? If yes — anonymize, describe instead of paste, or use an enterprise tool with data protection agreements.
Will someone rely on this output being factually correct? If yes — verify all statistics, confirm all citations exist, cross-reference legal or medical claims with authoritative sources. Give AI permission to express uncertainty.
Could this output unfairly affect or exclude anyone? Check for default assumptions about gender, race, age, or cultural background. Ask for multiple perspectives. Review hiring criteria, evaluations, and public-facing content with extra scrutiny.
Should the audience know AI was involved? Consider the stakes, your industry norms, and whether the context demands disclosure. When in doubt, disclose. Use professional language that frames AI as a tool, not a shortcut.
Apply the front page test: would you be comfortable if your AI use in this case appeared on the front page of your industry's top publication? If you hesitate — reconsider your approach. You own the output. Own the process too.
Build your personal AI ethics code.
Based on everything you've learned, write down 5 personal rules for how you'll use AI. Not abstract principles — concrete rules you'll actually follow. Here's an example:
2. I will fact-check all AI statistics before publishing.
3. I will disclose AI use for any content published under my name.
4. I will review AI output for bias before using it for hiring or evaluation.
5. I will treat AI as a tool, not an authority — my judgment always comes last.
Congratulations. You now understand AI ethics better than most people in the industry. The goal was never to make you afraid of AI — it was to make you wise about it. Use AI boldly, but use it well.
Review the core ethical principles from this course.
Apply the TRUST framework to real scenarios.
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