Facial recognition and surveillance debates
Understand how face recognition works, where it fails, and the arguments for and against its use
A taste of a lesson
If face recognition is 99% accurate, why are people worried about mistakes?
Because accuracy figures hide two things. First, scale: compare one face against a database of a million people and even a tiny false match rate per comparison can produce several wrong candidates. Second, distribution: if errors are higher for some groups, those people carry more of the risk. Also ask, 99% of what: verification or identification, which images, which threshold? Quick exercise: with a one in 100,000 false match rate per comparison, how many false matches would a search of one million faces produce on average?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Explain detection, verification and identification and how match thresholds work
- Describe documented accuracy disparities and why one to many search raises error risk
- Present the strongest arguments for and against specific uses
- Compare broad regulatory approaches to face recognition across jurisdictions
Lesson plan
- 1 How face recognition works Understand detection, verification, identification and match scores. Start
- 2 Accuracy and its unequal errors Learn what evaluations have found about error rates across groups. Start
- 3 Real cases and human review Examine documented harms and why human checking often fails. Start
- 4 The debate, steelmanned Weigh the strongest arguments on each side for specific uses. Start
- 5 How places regulate it Compare broad regulatory approaches without legal advice. Start
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About this tutor
For citizens, students, journalists and professionals who want an informed view on one of the most contested uses of AI. You learn the technology in plain terms: detection, verification and identification, match thresholds and why false matches happen. You study documented accuracy disparities and reported wrongful arrests, then examine the main debates: policing and public safety, shops and venues, workplaces, schools, border control, and protest monitoring. You weigh arguments on all sides and look at the broad regulatory approaches different places have taken, from bans to licensing to permissive use, without legal advice. You finish able to argue any position fairly.
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About the teacher
I teach AI ethics as practical judgment: privacy, fairness and accountability you can act on
9 tutors 327 lessons taught Sample
I teach the ethics of AI as something you do, not something you recite. My lessons cover privacy and personal data, bias and fairness, explainability and the effects of AI on work and high stakes decisions. I use real cases and simple methods so learners can reason through a new situation on their own. My background combines data analysis with...
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