The Book Prize Index

Your Face Belongs to Us: The Secretive Startup Dismantling Your Privacy

Kashmir Hill

*LONGLISTED FOR THE FINANCIAL TIMES BUSINESS BOOK OF THE YEAR AWARD* BEST BOOKS OF 2023 - FINANCIAL TIMES BEST TECHNOLOGY BOOKS OF 2023 - THE TIMES 'The dystopian future portrayed in some science-fiction movies is already upon us. Kashmir Hill's fascinating book brings home the scary implications of this new reality' JOHN CARREYROU, author of Bad Blood ______________________________________________________________________ When Kashmir Hill stumbled upon Clearview AI, a mysterious startup selling an app that claimed it could identify anyone using just a snapshot of their face, the implications were terrifying. The app could use the photo to find your name, your social media profiles, your friends and family - even your home address. But this was just the start of a story more shocking than she could have imagined. Launched by computer engineer Hoan Ton-That and politician Richard Schwartz, and assisted by a cast of controversial characters on the alt-right, Clearview AI would quickly rise to the top, sharing its app with billionaires and law enforcement.

TechnologyTechnology · medium confidenceTechnology81 signalsLlm Classifier: Technology -> TechnologyGoogle Books: Business & Economics -> Business & EconomicsKeyword Classifier: Business & Economics -> Business & EconomicsScience & DiscoveryTechnology, Computing & AIBusiness, Capitalism & CorporationsMedia, Journalism & Public Opinion

Award History

0 wins · 1 total

AwardYearResultCategory / Notes
Royal Society Science Book PrizeMajor2024ShortlistSecondary source
View all award history (1)
Experimental book profileGenerated by GPT-5.4 nano · may contain inaccuracies

This is an unverified interpretation of the catalog description, offered as an opt-in discovery experiment—not as bibliographic fact.

  1. 01 Central figures

    None extracted with sufficient confidence.

    These suggestions did not meet the normal display threshold and are more likely to be wrong.

    • Hoan Ton-That42%
    • Richard Schwartz42%

  2. 02 Central places

    None extracted with sufficient confidence.

  3. 03 Suggested argument

    No argument inferred with sufficient confidence.

  4. 04 Reading orientation

    30 / 100 academic

    Serious trade

    An estimate of intended readership and scholarly apparatus—not quality or importance.

Confidence percentages are the model's own estimates. Profile confidence: 63%.