1. Which organization, founded by Timnit Gebru after she left Google, focuses specifically on AI bias research and advocacy?
Correct. DAIR was founded by Timnit Gebru after her departure from Google. The Algorithmic Justice League was founded by Joy Buolamwini; Masakhane focuses on African language AI; Data for Black Lives works on health equity.
DAIR (Distributed AI Research Institute) was founded by Timnit Gebru. The Algorithmic Justice League was founded by Joy Buolamwini. Each organization focuses on a related but distinct area of AI accountability work.
2. Clearview AI's facial recognition database was built by scraping photos from public social media. Under US law at the time, this was:
Correct.
Review Lesson 4, Opening Scene. The Clearview case is significant precisely because its legal status was contested and varied by jurisdiction.
3. Training data bias occurs when:
Correct. Training data bias is the mechanism by which AI systems inherit and reproduce existing societal inequalities — not because anyone programmed discrimination explicitly, but because the historical data those inequalities are baked into became the AI's teacher.
Training data bias is when historical inequalities embedded in data get learned and reproduced by an AI. No explicit discrimination needs to be programmed — the AI learns the pattern from data that already encoded unequal treatment.
4. "Context collapse" refers to:
Correct.
Review Lesson 1, Section 2 — danah boyd's work on context collapse and the audience problem.
5. The Netherlands' SyRI algorithm flagged households for benefits fraud and was later ruled illegal by a Dutch court. What made the families' challenge effective, and what broader impact did it have?
Correct. The SyRI case shows the full arc: documented specific harm, legal challenge, court ruling, broader policy impact. Affected communities without technical expertise changed national and regional AI governance.
Not quite. The families documented the harm, pursued legal channels, won a human rights ruling, and set precedent that shaped EU AI regulation debates — all without technical expertise, through the power of documented, legally-framed pushback.
6. Why is "confabulation" considered a more precise term than "hallucination" for AI-generated false information?
Correct. "Hallucination" implies a system that is normally grounded in reality having a temporary lapse. Language models don't have a baseline of verified truth to depart from — confabulation (plausible gap-filling without truth-checking) better describes the structural situation.
Not quite. The key distinction is what each word implies about the system's baseline. Hallucination implies temporary departure from normal accuracy. Confabulation describes a structural process — generating plausible content without any truth-verification mechanism — which is accurate for language models.
7. What is the "Megaphone Effect" as described in this module?
Correct. The Megaphone Effect is about amplification — the same content comes back more polished, more forceful, and potentially more far-reaching.
Review Lesson 1. The Megaphone Effect describes how AI amplifies your existing words and ideas — making them clearer, more persuasive, and able to reach further — without necessarily changing their basic nature.
8. A student sees a short video clip of a teacher appearing to say something offensive. The clip is being shared widely in a group chat. Applying the concepts from this module, what is the most responsible immediate action?
Right. The emotional urgency a clip produces is exactly the moment to slow down — not speed up. Tracing the source and recognizing the limits of video as evidence is the calibrated response.
Sharing immediately or acting on it immediately are both premature. The clip might be real, manipulated, or completely fabricated. Short video clips shared in emotionally charged group chats are precisely the format deepfakes are designed for. Pause and investigate before acting.
9. When you comment on a misleading post to argue against it, the most likely algorithmic effect is:
Correct. Engagement algorithms track interaction volume, not sentiment. A rebuttal comment is still a comment — still engagement data — which can increase the post's algorithmic visibility. This is one of the counterintuitive dynamics of the engagement optimization model.
Engagement algorithms don't read the content of comments — they count them. A comment arguing against a post still signals engagement, which can increase how widely the algorithm distributes that post.
10. A filter bubble forms when:
Correct. A filter bubble is a natural consequence of recommendation algorithms doing their job: they show you more of what you've engaged with, which progressively narrows your information environment to content that confirms what you already believe.
A filter bubble is created by algorithmic personalization, not deliberate censorship or user choice. The algorithm narrows your information environment by optimizing for what you'll engage with — not what's diverse or challenging.
11. Amazon's résumé-screening AI penalized women's organizations on résumés because:
Correct. Historical bias: the AI learned from who had been hired in the past — mostly men — and treated that pattern as a predictor for future success.
No deliberate rule was coded. The bias emerged from training data that overrepresented male hires, making maleness an implicit success signal.
12. Joy Buolamwini's "Gender Shades" research (2018) found that facial recognition systems were most accurate for:
Correct. Lighter-skinned men had near-perfect accuracy while darker-skinned women had error rates up to 35% — directly reflecting which groups were most represented in training data.
Buolamwini found the highest accuracy for lighter-skinned men (near 99%) and the lowest for darker-skinned women (error rates up to 35%).
13. Someone tells you an AI chatbot is producing false criminal accusations about their neighbor. What should be the very first step?
Correct. Documentation is always the foundation. Every subsequent step — reporting to the platform, contacting advocacy organizations, supporting legal action — requires a precise, timestamped record of what was said and how it was generated.
Not quite. Legal action and public posting both require evidence. Without precise documentation — screenshot, prompt, date, platform — you have nothing concrete to work with. Documentation is always step one.
14. According to YouTube's internal 2019 data, what percentage of time spent on the platform came from algorithmic recommendations rather than user searches?
Correct — 70%. This means most of what YouTube users watched was chosen by the algorithm, not by the users themselves. That's a significant amount of unsolicited curation.
Review Lesson 2. YouTube's internal study found 70% of watch time came from algorithmic recommendations. Most of what people watched on YouTube, they hadn't actively searched for.
15. A researcher from a community directly affected by a biased AI system is best positioned to notice the problem because:
Exactly. This is the module's closing argument: situated experience is a form of expertise. Joy Buolamwini noticed the problem when she saw it on her own face. That observation, precisely described, changed the field. The lesson applies to you directly.
The lesson closes by making this explicit: experiencing the gap, describing it precisely, and connecting it to a pattern is itself expertise. Buolamwini's work started when she noticed the system worked poorly on her own face — that situated observation drove major industry change.
16. Robert Williams's wrongful arrest in Detroit in 2020 demonstrated that:
Correct. Buolamwini's research had been published and covered widely before Williams's arrest. Knowing about bias didn't stop deployment. His case shows that accountability requires the full cycle: documentation, legal action, sustained public attention, and policy response.
The key point of Williams's case is that research documenting the bias already existed — and the system was still deployed. Accountability required more than published research: it required litigation, publicity, and political pressure.
17. Amazon's AI hiring tool began penalizing résumés containing the word "women's" — for example, "women's chess club" — without being programmed to do so. What type of bias does this represent?
Correct. Historical bias occurs when AI learns from data reflecting past inequalities and reproduces those inequalities in future outputs. The tool learned that maleness correlated with success in its training data — and treated that correlation as a rule.
Not quite. This is historical bias: the AI was trained on hiring data from a period and industry where women were underrepresented, so it learned to treat markers associated with women as negative signals.
18. A human being is described as being "in the loop" when an AI makes a recommendation and a human must confirm before action is taken. What makes this protection less effective than it sounds?
Correct. Automation bias is the specific mechanism that hollows out human-in-the-loop protections: when humans consistently defer to AI rather than genuinely reviewing it, the accountability gap between "AI suggested" and "human confirmed" disappears.
Not quite. The danger is automation bias: humans required to review AI recommendations often accept them with minimal scrutiny, especially under time pressure — meaning "a human confirmed it" doesn't actually mean a human independently evaluated it.
19. Frances Haugen's disclosure of Facebook's internal research revealed that company researchers had documented:
Correct. This is significant precisely because it shows the company had internal documentation of the problem before it became public — knowledge of the harm did not automatically produce change.
Haugen's disclosure revealed internal research showing the algorithm amplified divisive content and pushed users toward extremes — research the company possessed before it became public knowledge.
20. The EU AI Act (2024) treats real-time biometric surveillance in public spaces as:
Correct. The EU AI Act largely prohibits real-time biometric surveillance in public — a direct policy response to documented harms from facial recognition systems like the one in the Williams case.
Real-time biometric surveillance in public is one of the EU AI Act's most strictly treated categories — largely prohibited, with only narrow exceptions defined in the law.