Buolamwini is best known for demonstrating that facial analysis software performs worst on women with darker skin tones, but also advocates for greatly increased regulation and oversight of facial analysis tools, against their use by military or law enforcement, and fights to limit their use against marginalized people across areas as diverse as hiring, housing, and health care.9

onsider an algorithm for university admissions. An (individualized) algorithmic fairness approach attempts to ensure that any two individuals with the same profile, but who differ only by, say, gender, receive the same recommendation (admit/waitlist/decline). Auditing an admissions algorithm under the assumptions of algorithmic fairness can be conducted through paired-test audits: submit a group of paired, identical applications, but change only the gender of one of the applicants in each pair and observe whether the system produces the same recommendation for each. If the algorithm recommends admission for more men than women (at a statistically significant level) in otherwise identical paired applications, we can say that it is biased against women. It needs to be retrained and reaudited to ensure that this bias is eliminated. This is the approach proposed by most of the researchers and practitioners working on algorithmic bias today

The Design of Everyday Things is a canonical design text. It's full of useful insights and compelling examples. However, it almost entirely ignores race, class, gender, disability, and other axes of inequality. Norman very briefly states that capitalism has shaped the design of objects,17 but says it in passing and never relates it to the key concepts of the book. Race and racism appear nowhere. He uses the term women only once, in a passage that describes the Amphitheatre Louis Laird in the Paris Sorbonne, where “the mural on the ceiling shows lots of naked women floating about a man who is valiantly trying to read a boo

Design justice brings this insight to the fore and calls for designers' ongoing attention to the ways these differences are shaped by the matrix of domination.