Identifying and Reducing Bias
Understand sources of bias in LLM outputs and develop prompting techniques to mitigate biased responses.
Understanding LLM Bias
What is bias in an LLM? It's when the model's output unfairly favors or disfavors certain groups, ideas, or demographics. This often happens because the vast amounts of data LLMs learn from reflect existing societal prejudices.
Recognizing and actively working to reduce bias is crucial for developing fair, ethical, and reliable AI systems.
Sources of LLM Bias
LLM bias primarily stems from its training data. If the data contains historical or societal prejudices, the LLM will learn and inadvertently reflect them. Common sources include:
- Internet Text: Web data, books, and articles often contain stereotypes or imbalanced representation.
- Human Annotators: Biases can be introduced or reinforced during data labeling and fine-tuning processes.
- Algorithmic Design: Certain model architectures or training objectives can sometimes inadvertently amplify existing biases within the data.
All lessons in this course
- Identifying and Reducing Bias
- Strategies for Reducing Hallucinations
- Ethical Considerations in Prompting