Summary
In this chapter, we learned about fairness and bias in LLMs, focusing on understanding different fairness definitions, such as demographic parity, equal opportunity, and equalized odds. We explored the types of bias that can emerge in LLMs, including representation, linguistic, allocation, quality of service, and stereotypical, along with techniques for detecting and quantifying them through metrics such as demographic parity difference and equal opportunity difference.
We used practical coding examples to show you how to analyze bias. Debiasing strategies such as data augmentation, bias-aware fine-tuning, and fairness-aware training were also covered, providing actionable ways to mitigate bias. Finally, we gained insights into ethical considerations, including transparency, diverse development teams, regular auditing, and user feedback systems. These skills will help you detect, measure, and address bias in LLMs while building more equitable and transparent AI systems.
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