Bias, Fairness, and Transparency

Welcome! In this lesson, we'll tackle a critical topic: bias, fairness, and transparency in AI agents. As AI becomes more powerful, ensuring it acts fairly and predictably is essential.

AI bias occurs when an AI system produces results that are systematically prejudiced or unfair towards certain groups or individuals. This can lead to discriminatory outcomes.

Where does bias come from? Often, it's not intentional but a reflection of the data and processes used to build AI systems, especially Large Language Models (LLMs).

• Training Data: If the data used to train an LLM contains societal biases (e.g., historical stereotypes), the model will learn and perpetuate them.
• Human Labeling: Biases can be introduced during data annotation or reinforcement learning from human feedback.
• Model Design: Sometimes, the architecture or algorithms themselves can inadvertently amplify biases.