Intersectionality explores how overlapping social identities—such as race, gender, class, and sexuality—interact to create unique experiences of discrimination. In AI, failing to consider intersectionality often leads to gender bias that disproportionately impacts women of color, LGBTQ+ individuals, and others with intersecting marginalized identities. This means AI systems might perform well for one subset of women but poorly for others, underscoring the need for more nuanced data and modeling.
- Log in or register to contribute
Contribute to three or more articles across any domain to qualify for the Contributor badge. Please check back tomorrow for updates on your progress.