Improving the algorithm
Ethan Zuckerman
I first learned about AI and bias in 2016 from my Masters student Joy Buolamwini. She was constructing a digital “magic mirror” that would blend the image of her face with that of one of her heroes, Serena Williams. For the project to work, an algorithm needed to identify Joy’s facial features so it could map her eyes, nose and mouth to the computer-generated image of the tennis star.
In a video Joy made, the algorithm identified the facial features of her Asian-American colleague immediately. Faced with Joy’s darker face, it failed entirely. Then Joy put on a featureless white mask and the algorithm sprang into action.
It was a profound demonstration of how Joy’s real face was entirely invisible to an algorithm that could identify a cartoonishly simple face, so long as it was white.
Buolamwini’s video turned into an influential audit of the racial and gender biases of machine vision systems, a doctoral dissertation and eventually the Algorithmic Justice League, an advocacy organisation focused on fairness in artificial intelligence. And it sent me down the path of considering which technical problems could be solved with better data, and which ones require us to build better societies.
Lisez l'article complet et bien d'autres dans ce numéro de
Prospect Magazine
Options d'achat ci-dessous
Si le problème vous appartient,
Connexion
pour lire l'article complet maintenant.
Numéro unique numérique
March 2022
Ce numéro et d'autres anciens numéros ne sont pas inclus dans une nouvelle version de l'article
abonnement. Les abonnements comprennent le dernier numéro régulier et les nouveaux numéros publiés pendant votre abonnement.
Prospect Magazine