AI in Law Enforcement and Policing Quiz

Answers and explanations appear as you take the quiz.

1. What did NIST face-recognition evaluations report about demographic error-rate differences?
   • Most tested algorithms showed demographic differentials, with size depending on algorithm and application
   • All tested algorithms had identical error rates for every demographic group
   • The study found demographic differences only in license-plate readers
   • NIST concluded that image quality never affects error rates
2. Why do critics say predictive policing can amplify bias?
   • It uses no data at all
   • It learns from arrest data that reflects past over-policing
   • It only predicts the weather
   • It ignores location entirely
3. What does a facial recognition system create from a face image?
   • A paper sketch
   • A fingerprint
   • A numerical embedding or 'faceprint'
   • A DNA sequence
4. ShotSpotter is an example of which type of system?
   • License-plate reader
   • Facial recognition
   • DNA analysis
   • Acoustic gunshot detection
5. Automated license-plate readers primarily:
   • Log vehicle locations at scale
   • Translate foreign languages
   • Measure air quality
   • Issue passports
6. How does the EU AI Act treat many law-enforcement AI uses?
   • As unregulated
   • As high-risk or prohibited
   • As mandatory for all police
   • As entertainment software
7. In a face-recognition system, what can changing the match-similarity threshold trade off?
   • Image resolution against video length
   • Database size against audio quality
   • False matches against missed matches
   • Encryption strength against network speed
8. What can AI transcription add to a body-worn-camera reporting workflow?
   • A determination of guilt without a court
   • A facial-recognition match from audio alone
   • An automatic guarantee that a report is accurate
   • A searchable transcript or draft narrative based on recorded audio