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Mitigating Bias in AI Language Models
This chapter emphasizes the critical need for bias mitigation in large language models, addressing various biases related to health, race, gender, and religion. It explores the impact of these biases, particularly in law enforcement and financial services, and their effect on marginalized communities. Through real-life examples and a discussion on the complexities of cultural sensitivity, the chapter underscores the societal implications of automated decision-making and the importance of questioning data integrity.
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