More than a Glitch
Confronting Race, Gender, and Ability Bias in Tech
-
- ¥3,000
発行者による作品情報
When technology reinforces inequality, it’s not just a glitch—it’s a signal that we need to redesign our systems to create a more equitable world.
The word “glitch” implies an incidental error, as easy to patch up as it is to identify. But what if racism, sexism, and ableism aren’t just bugs in mostly functional machinery—what if they’re coded into the system itself? In the vein of heavy hitters such as Safiya Umoja Noble, Cathy O’Neil, and Ruha Benjamin, Meredith Broussard demonstrates in More Than a Glitch how neutrality in tech is a myth and why algorithms need to be held accountable.
Broussard, a data scientist and one of the few Black female researchers in artificial intelligence, masterfully synthesizes concepts from computer science and sociology. She explores a range of examples: from facial recognition technology trained only to recognize lighter skin tones, to mortgage-approval algorithms that encourage discriminatory lending, to the dangerous feedback loops that arise when medical diagnostic algorithms are trained on insufficiently diverse data. Even when such technologies are designed with good intentions, Broussard shows, fallible humans develop programs that can result in devastating consequences.
Broussard argues that the solution isn’t to make omnipresent tech more inclusive, but to root out the algorithms that target certain demographics as “other” to begin with. With sweeping implications for fields ranging from jurisprudence to medicine, the ground-breaking insights of More Than a Glitch are essential reading for anyone invested in building a more equitable future.
PUBLISHERS WEEKLY
"The biases embedded in technology are more than mere glitches; they're baked in from the beginning," argues Broussard (Artificial Intelligence), a data journalism professor at New York University, in this scathing polemic. Telling the stories of individuals from marginalized communities who have been wronged by technology, the author shows how design and conceptual failures produce unfair outcomes. She describes how a Black man was arrested by Detroit police because a facial recognition algorithm incorrectly flagged him as a match for a shoplifter, reflecting the tendency of such programs to produce false matches for people of color, who are underrepresented in the images used to train those programs. Other case studies are Kafkaesque, such as the Black Chicago man who was shot twice under suspicion of being a snitch because police cars frequently parked outside his house after predictive policing software identified him as at risk for gun violence. The author condemns "technochauvinism," or the belief that "computational solutions superior to all other solutions," as exemplified by the story of a Deaf Apple Store employee who was denied an on-site interpreter because Apple preferred alternative, inadequate solutions that used its products. The stories enrage and drive home the cost of the failures and prejudices built into ostensibly cutting-edge programs. This sobering warning about the dangers of technology alarms and unsettles.
