LLM Bias, Fairness, and Ethical Considerations

Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. Large language models (LLMs) require careful handling of bias, fairness, and ethics so they don't unconsciously learn and replicate data biases. It's vital to design high-performing LLMs that uphold ethical standards, facilitating trustworthy AI that respects user privacy, complies with regulatory standards, and supports positive societal impacts. In this course, learn how to identify and address common types of LLM output bias and evaluate LLM output fairness. Next, discover the challenges of deploying LLMs in regulated industries, and compliance issues and ethical concerns for sensitive domains. Finally, explore LLM fine-tuning bias mitigation techniques for reducing bias in real-world applications and the long-term implications of AI ethics in LLM advancements. After completing this course, you will be able to outline LLM bias, fairness, and ethical considerations.