Technology

37691 readers

316 users here now

A nice place to discuss rumors, happenings, innovations, and challenges in the technology sphere. We also welcome discussions on the intersections of technology and society. If it’s technological news or discussion of technology, it probably belongs here.

Remember the overriding ethos on Beehaw: Be(e) Nice. Each user you encounter here is a person, and should be treated with kindness (even if they’re wrong, or use a Linux distro you don’t like). Personal attacks will not be tolerated.

Subcommunities on Beehaw:

This community's icon was made by Aaron Schneider, under the CC-BY-NC-SA 4.0 license.

founded 2 years ago

MODERATORS

[email protected]

[Corp Blog] Facial recognition tech lands innocent woman with bogus carjacking charge (www.malwarebytes.com)

submitted 1 year ago* (last edited 1 year ago) by [email protected] to c/[email protected]

14 comments fedilink hide all child comments

Summary

Detroit woman wrongly arrested for carjacking and robbery due to facial recognition technology error.
Porsche Woodruff, 8 months pregnant, mistakenly identified as culprit based on outdated 2015 mug shot.
Surveillance footage did not match the identification, victim wrongly identified Woodruff from lineup based on the 2015 outdated photo.
Woodruff arrested, detained for 11 hours, charges later dismissed; she files lawsuit against Detroit.
Facial recognition technology's flaws in identifying women and people with dark skin highlighted.
Several US cities banned facial recognition; debate continues due to lobbying and crime concerns.
Law enforcement prioritized technology's output over visual evidence, raising questions about its integration.
ACLU Michigan involved; outcome of lawsuit uncertain, impact on law enforcement's tech use in question.

you are viewing a single comment's thread
view the rest of the comments

[–] [email protected] 17 points 1 year ago

This is the issue with big data. Big enough database you will find a closest match that seems pretty good. False positives are a huge concern in any big data approach... and couple that with lazy policing well you get this.