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Book Overview & Buying
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Table Of Contents
LLMs in Enterprise
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RAI is essential for ethical, legal, and safe LLM operation. These models pose unique challenges due to their scale, probabilistic outputs, and vast training data, risking emergent behaviors, amplified biases, and data memorization. RAI addresses this through fairness, transparency, accountability, and safety. Compliance is increasingly mandated by regulations such as the EU AI Act and NIST AI RMF, carrying significant penalties.
LLMs introduce key ethical risks: bias amplification, privacy violations, and harmful content generation. Bias, often from skewed training data, is mitigated by pre-processing, adversarial debiasing, and toxicity filters. Privacy concerns from data memorization are tackled with both basic pre-processing techniques, such as masking sensitive details and data sanitization, and more advanced methods, such as DP and FL. Countering “jailbreaking” attacks requires multi-layered moderation using classifiers, fact-checkers, and input sanitization...