Bias, Fairness & Explainability in LLMs
Identify and mitigate biases in LLM outputs, ensuring fairness and striving for explainability in AI-driven decisions.
Understanding Bias in LLMs
What is bias? It's an unfair inclination for or against something. In Large Language Models (LLMs), bias means the model's outputs might unfairly favor certain groups or perspectives, often reflecting biases present in its training data.
Recognizing and addressing bias is crucial for developing ethical and reliable AI applications.
Sources of LLM Bias
LLMs learn from vast amounts of text and code. If this data contains societal biases (e.g., stereotypes in news articles, historical prejudices), the model can learn and perpetuate them. Key sources include:
- Training Data: The primary source, reflecting real-world societal biases.
- Human Annotation: Biases introduced during data labeling or fine-tuning.
- Algorithmic Design: Less common, but model architecture choices can amplify existing biases.
All lessons in this course
- Bias, Fairness & Explainability in LLMs
- Ethical Prompt Design
- Emerging Research & Future Directions
- Privacy & Data Protection in LLM Prompts