1. The Word Embedding Association Test (WEAT) showed that GloVe and word2vec embeddings:
✓ Correct — Correct. Caliskan et al. (Science, 2017) showed that WEAT effect sizes closely matched those from human IAT studies, demonstrating that bias in training corpora is faithfully encoded in embedding geometry.
Incorrect. WEAT demonstrated that embeddings trained on large text corpora reproduce human implicit bias results — the statistical patterns of biased language use are encoded in embedding space.
2. A 2021 JAMA Dermatology review of 70 AI dermatology studies found what about skin tone representation in training data?
Correct. The review found only 18% of studies classified skin tone at all, and among those, darker skin tones were dramatically underrepresented — a systematic gap in medical AI development.
Incorrect. Only 18% of the 70 studies even classified skin tone, and darker tones were dramatically underrepresented — reflecting historical inequities in who received specialist dermatology care.
3. Which EU AI Act risk category applies to a promotion recommendation AI used by employers in EU member states?
Correct. Annex III of the EU AI Act explicitly lists AI in employment decisions, including promotion, as High Risk — triggering conformity assessment, technical documentation, and fundamental rights impact assessment obligations.
Promotion recommendation AI falls under Annex III's employment AI High Risk category — requiring conformity assessment, registration in the EU database, and a fundamental rights impact assessment before deployment.
4. Which city became the first in the United States to ban government use of facial recognition technology?
Correct. San Francisco's Board of Supervisors voted in May 2019 to prohibit city agencies — including police — from using facial recognition technology. Oakland and Boston followed later in 2019 and 2020 respectively.
San Francisco was first, in May 2019. Oakland followed in July 2019 and Boston in June 2020. Detroit, notably, was the site of Robert Williams's wrongful arrest due to facial recognition — but did not ban the technology in 2020.
5. COMPAS did not use race as a direct input. What mechanism produced racially disparate outputs?
Correct. Variables like arrest history reflect unequal policing by neighborhood and race, encoding structural inequality as a proxy for race.
Incorrect. Proxy variables — arrest history, employment, neighborhood — carry racial correlations due to systemic inequality, producing disparate outcomes without using race directly.
6. The Amazon hiring AI was trained on ten years of résumés that led to successful hires. What was fundamentally wrong with using historical hires as the definition of "a good candidate"?
Correct. Past hiring reflected bias — conscious or structural — so "success" in the training data was defined within a discriminatory context that the model learned to reproduce.
Incorrect. The core problem is that historical hires were shaped by historical discrimination, so training a model to replicate those outcomes trains it to replicate those biases.
7. What is the primary purpose of "model cards" as introduced by Mitchell and Gebru?
Correct. Model cards are structured disclosures — analogous to nutritional labels — covering training data, performance broken down by subgroup, intended use cases, and known limitations.
Model cards are structured disclosures accompanying AI models, analogous to nutritional labels — covering training data sources, fairness metrics by demographic group, intended uses, and known failure modes.
8. What does "model drift" require organizations to do to maintain fairness over time?
Correct. Model drift means a model that was fair at deployment can become unfair as the world changes — requiring continuous monitoring and re-auditing on live production data, as required by the EU AI Act for high-risk systems.
Model drift requires ongoing monitoring — defining monitoring metrics at deployment time and re-auditing on production data periodically, not just once at launch. The EU AI Act's high-risk requirements mandate post-market monitoring systems.
9. Under the US four-fifths rule, if the highest-scoring group has a selection rate of 60%, disparate impact is presumed when a protected group's rate falls below:
✓ Correct — Correct. 60% × 0.80 = 48%. Below this rate, disparate impact is legally presumed under the 1978 Uniform Guidelines.
Incorrect. The four-fifths rule: 60% × 0.80 = 48%. Any group with a selection rate below 48% triggers presumed disparate impact.
10. When Amazon's engineers removed gender-associated terms from the model's features, what happened?
Correct. This is the "whack-a-mole" problem — removing one biased feature causes the model to find and use other correlated signals to achieve the same biased outcome.
Incorrect. The bias persisted through other features — this is called whack-a-mole debiasing, where fixing one signal causes the model to compensate with others.
11. Amazon's AI recruiting tool, scrapped in 2017, penalized résumés containing the word "women's." What category of bias entry point does this represent?
Correct. The model learned patterns from Amazon's actual historical hiring — which disproportionately resulted in male candidates being hired. It then generalized those patterns, treating female-associated résumé features as predictive of failure to be hired.
This is training data composition bias. The model was trained on what Amazon's historical hiring data defined as "success." Because that history was male-dominated, the model learned to devalue anything correlated with female identity. The problem originated in the data, not in the objective function or post-processing.
12. Historical FHA redlining (1934–1968) is relevant to modern ML models primarily because:
✓ Correct — Correct. The residential segregation created by redlining is measurable today in zip code demographics, property values, and economic indicators — all common ML features that inherit this historical discrimination.
Incorrect. Redlining laws ended with the Fair Housing Act of 1968. Their relevance today is that they created persistent residential segregation patterns that make geographic features like zip code act as racial proxies in current ML models.
13. Demographic parity requires that:
✓ Correct — Correct. Demographic parity holds when the rate of positive predictions is the same regardless of group membership.
Incorrect. Demographic parity is specifically the equal positive prediction rate across groups — not TPR equality (equalized odds), score calibration, or individual treatment.
14. The 2018 Gender Shades study tested facial recognition systems from which three vendors?
Correct. Buolamwini and Gebru tested Microsoft, IBM, and Face++, finding error rates up to 34.7% for darker-skinned women.
The Gender Shades study tested Microsoft, IBM, and Face++. All three vendors subsequently updated their systems after the audit results were published.
✓ Correct — Correct. Historical bias in training data caused the model to replicate past discriminatory hiring decisions.
Incorrect. The bias was unintentional — the model learned from historically male-dominated hiring data.
16. Why are post-processing fairness techniques useful for third-party vendor models?
Correct. Post-processing techniques like equalized odds threshold adjustment operate on output scores only — no access to model weights or training data required, making them the only option for black-box vendor systems.
Post-processing techniques are valuable precisely because they work on outputs alone — useful when you have no access to a vendor model's internals, which is common in enterprise AI procurement.
17. Demographic parity requires which condition?
Correct. Demographic parity (also called statistical parity) requires that the same proportion of each group receives a positive prediction — without regard to whether those predictions are correct.
Demographic parity requires equal positive prediction rates across groups. Option A is equal opportunity. Option C is equalized odds. Option D is calibration. These are four distinct fairness criteria.
18. What did the 2020 New England Journal of Medicine study find about pulse oximeter accuracy by race?
Correct. The study found Black patients experienced occult hypoxemia — undetected dangerously low oxygen — at nearly triple the rate of white patients.
Incorrect. The study found Black patients were nearly three times more likely to have dangerously low oxygen undetected by pulse oximeters due to melanin interference with the device's optical sensors.
19. What does the fairness impossibility theorem (Chouldechova and Roth, 2019) establish?
Correct. The impossibility theorem proves that simultaneous satisfaction of calibration, equal false positive rates, and equal false negative rates is mathematically impossible when group base rates differ — practitioners must choose.
The impossibility theorem is more specific: when groups have different base rates, you provably cannot simultaneously achieve calibration, equal FPR, and equal FNR — you must prioritize one criterion over the others.
20. The Chouldechova impossibility theorem establishes that calibration and equalized odds cannot both be satisfied simultaneously unless which condition holds?
Correct. Equal base rates are the necessary and sufficient condition for simultaneous satisfaction of calibration and equalized odds. When groups have different actual outcome rates, these two criteria are mathematically incompatible.
The impossibility is a mathematical property that holds regardless of dataset size or model type. It disappears only when base rates are equal across groups — which the real-world data often does not provide.