From the course: The AI Equity Imperative: Building a More Inclusive Future with AI
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Intersectional impacts
From the course: The AI Equity Imperative: Building a More Inclusive Future with AI
Intersectional impacts
- Intersectionality in AI examines how multiple aspects of social identity, like race, ethnicity, religion, and socioeconomic status can create unique experiences of advantage or disadvantage when interacting with AI systems. When AI systems fail to account for intersectional identities, they can compound and amplify existing societal biases, creating harmful consequences for marginalized communities. AI systems often compound biases based on overlapping sociodemographic factors in complex ways. Gender bias in AI frequently intersects with other identities such as race, socioeconomic status, religion, disability status, and geographic location, creating compounding forms of discrimination. Prominent research examining intersectional impacts has shown how facial recognition technologies have demonstrated significantly higher error rates for women with darker skin tones compared to men with lighter skin tones with showing how gender and racial biases can interact. In healthcare…
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