Do You See What I See? Building AI for All of Us
Assembly Required with Stacey Abrams
Bias in AI: Unpacking Disparities
This chapter examines racial and gender biases in artificial intelligence, particularly in facial recognition systems, through the lens of a researcher's findings. It underscores the significance of equitable and representative data in AI development to avoid perpetuating existing inequalities. The conversation raises critical questions about the impact of biased technologies on marginalized communities and the importance of accountability in AI practices.
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