Boogaloo Bias
Facial Recognition by Any Memes Necessary

Boogaloo Bias is an interactive artwork and research project that highlights some of the known problems with law enforcement agencies’ use of facial recognition technologies.

These practices include ‘brute forcing’ where, in the absence of high-quality images of a suspect, agents have been known to substitute images of celebrities the suspect is reported to resemble. To lampoon this approach, the Boogaloo Bias facial recognition algorithm is trained only on faces of characters from the 1984 movie Breakin’ 2: Electric Boogaloo to search for members of the anti-law enforcement militia, the Boogaloo Bois. The film is the namesake for the Boogaloo Bois, who emerged from 4chan meme culture and have been present at both right and left-wing protests in the US since January 2020. The system is used to search live video feeds, protest footage, and images that are uploaded to the Boogaloo Bias website. All matches made by the system are false positives. No information from the live feeds or website uploads is saved or shared.

Boogaloo Bias raises questions about automated decision making, public accountability, and oversight within a socio-technical system where machines are contributing to a decision-making process. Facial recognition technology allows for the quick surveillance of hundreds of people simultaneously and the ability to automate decisions using artificial intelligence, establishing a power structure controlled by a technocratic elite. Rather than providing a solution for how to improve facial recognition, Boogaloo Bias pushes the logic behind the current uses of facial recognition in law enforcement to an extreme, highlighting the absurdity of how this technology is being developed and used. Law enforcement currently uses images of celebrity doppelgängers to find suspects. In Boogaloo Bias, the corpus of training images is based solely on fictional characters, leading only to false positives.

Visit the Boogaloo Bias website

This project is a collaboration with Jennifer Gradecki.

Screen shots