Module Check
You have covered a lot of ground. AI ethics, in ten lessons, touches some of the most contested and consequential questions of your generation: what fairness means and who defines it, how misinformation spreads and how to fight it, who bears responsibility when AI causes harm, and what it means to be a thoughtful AI user yourself. This final lesson reviews the core ideas. Some of these questions will challenge you — the goal is not just to recall facts but to synthesize what you have learned into genuine understanding. If something feels unfamiliar, look back at the lesson where it was introduced. Every concept listed here was covered in this module.
Flashcards — click each card to reveal the answer
Comprehensive Review
Robert Williams was arrested after a facial recognition system wrongly matched his face to a suspect. This case illustrates which combination of problems? (Lessons 1 and 3)
A hiring AI trained on ten years of company data begins penalizing résumés that mention women's campus organizations. Which source of bias is this, and what lesson covered it? (Lesson 2)
A facial recognition tool is 99.1% accurate overall but only 72% accurate for dark-skinned women. The team reports only the 99.1% figure. What principle from Lesson 3 are they violating?
Someone sends you a video of a public figure making a shocking announcement. Applying what you learned in Lesson 4 and Lesson 5, what are the TWO most important verification steps?
A judge uses a recidivism prediction AI to help decide sentences. The AI gives a risk score but does not explain why. The judge approves all high-risk scores as maximum sentences without independent review. Which TWO concepts from this module apply? (Lessons 8 and previous)
From Lesson 7: A student asks whether AI will 'take all the jobs.' Based on this module, what is the most accurate and honest response?
Every lesson in this module connects to one core insight: AI systems are not neutral. They encode choices — about data, about what to optimize, about who to test on, about who bears accountability. Making those choices visible, deliberate, and subject to democratic accountability is what AI ethics is for. You now have the vocabulary and the frameworks to participate in that conversation.
Write a Policy Brief
- You are advising a city council considering deploying a facial recognition system in public transit stations to help solve crimes.
- Using concepts from across this module, write a one-page policy brief (aim for five to seven substantial paragraphs) that addresses:
- What are the potential benefits of this deployment? (Be specific and honest — do not dismiss real benefits.)
- What are the risks — drawing on accuracy disparities, surveillance creep, the chilling effect, and the accountability gap?
- What conditions, if any, would make this deployment ethical? Think about: accuracy thresholds by subgroup, data retention limits, human oversight requirements, transparency to the public, and an accountability structure.
- What is your recommendation, and on what grounds?
- Your brief should draw on at least five lessons from this module and name the concepts you are applying. Write as if you are advising real decision-makers — be substantive, be honest about trade-offs, and take a clear position that you can defend.