Unveiling Biases in AI Training and Decision-Making

The chapter explores how biases from human input infiltrate AI systems during training, affecting their decision-making processes. It discusses instances of data-driven bias, like defining beauty from a narrow perspective, and the complexities of training AI on subjective vs calculatable concepts. It emphasizes the importance of human oversight in AI to correct biases, prevent objectionable content, and ensure ethical behavior.