Gender Shades and Facial Recognition Bias Audits Quiz

Answers and explanations appear as you take the quiz.

1. Which companies' face analysis services did the original Gender Shades study audit?
   • Google, Apple and Meta
   • Amazon, Kairos and Clearview
   • IBM, Microsoft and Face++
   • NEC, Idemia and Cognitec
2. What was the Pilot Parliaments Benchmark made of?
   • Images of members of parliament from three African and three European countries
   • Celebrity photos scraped from the web
   • Mugshots from US police departments
   • Synthetic faces generated by a neural network
3. Which scale did the researchers use to label skin type?
   • The Monk Skin Tone scale
   • The Fitzpatrick scale
   • The Munsell color system
   • A self-reported race category
4. What task did Gender Shades actually measure?
   • One-to-many face identification against police databases
   • Emotion recognition
   • Face detection in video
   • Gender classification
5. Which subgroup had the highest error rates in Gender Shades?
   • Darker-skinned women
   • Lighter-skinned women
   • Darker-skinned men
   • Lighter-skinned men
6. What did the 2019 "Actionable Auditing" follow-up find?
   • No vendor had changed its system
   • All disparities had been eliminated industry-wide
   • Audited companies had narrowed their gaps, while unaudited vendors showed similar disparities
   • The original study's data had been fabricated
7. Why can a single similarity threshold produce unequal outcomes across groups?
   • Thresholds only apply to men
   • The same threshold can yield very different false match rates for different groups
   • Thresholds are chosen at random for each query
   • Thresholds affect speed but not errors
8. Which vendor response happened in June 2020?
   • Microsoft began selling facial recognition to all US police
   • Face++ published its training data
   • NIST banned facial recognition
   • IBM said it would leave the general-purpose facial recognition business