Algorithmic Fairness and Social Impacts

This chapter explores the hidden biases in algorithmic systems that are designed to be neutral, particularly focusing on their impact on marginalized communities. Through case studies such as Chicago's traffic cameras and the role of Clearview AI in law enforcement, it highlights the dangers of relying on perceived unbiased technologies. Additionally, it examines historical efforts in addressing social inequities and the importance of collaboration in driving meaningful change.

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