Data FeminismBooks

A powerful work by NYU professor, data scientist, and one of the few Black women AI researchers, Meredith Broussard. Reveals how tech neutrality is a myth and algorithms need accountability. From facial recognition only trained on lighter skin tones, to mortgage algorithms encouraging discriminatory lending, to dangerous feedback loops in medical diagnostic algorithms. Solution isn't making omnipresent tech more inclusive, but rooting out algorithms that target demographics as ‘other.’

Kate Crawford maps AI through minerals, energy, hidden labor, training data, classification, and state surveillance, revealing the extraction and concentrated power beneath machine intelligence.

It proposes seven practical principles that bring power analysis, intersectionality, situated knowledge, and co-design into data work itself.

Starting from trans airport screening and disability justice, Sasha Costanza-Chock argues for design led by affected communities, placing benefit, ownership, and accountability above expert good intentions.

Ruha Benjamin’s New Jim Code framework shows how racial hierarchy can be engineered, hidden by defaults, intensified through surveillance, or repackaged as a benevolent technical fix.

A shocking exposé into how the world is designed for men as the 'default,' systematically ignoring women's data. Spanning healthcare, technology, urban planning, and economics, Caroline Criado Perez reveals how invisible data bias seriously impacts women's health, safety, and lives.

Safiya Umoja Noble begins with the stigmatization of Black girls in commercial search to expose how advertising markets, platform monopoly, and knowledge classification produce algorithmic oppression.

Investigating welfare, homeless housing, and child protection, Virginia Eubanks shows how automation disguises austerity as neutral efficiency and subjects poor people to continuous surveillance.