Bias and Fairness
Summary
The subtopic of Bias and Fairness in AI alignment research focuses on the challenges posed by increasingly generalizable and flexible models, such as CLIP in computer vision. These models, while offering improved capabilities and reduced need for task-specific training data, also introduce new concerns regarding bias manifestation and fairness. As models become more adaptable to various tasks and allow for natural language specification of classification categories, the ways in which biases present themselves can shift and become less predictable. Research in this area emphasizes the importance of moving beyond traditional accuracy metrics when evaluating model performance, and instead considering a broader range of factors including different use contexts and the diverse individuals who may interact with the model. This shift in perspective is crucial for developing AI systems that are not only capable but also safe and fair when deployed in real-world scenarios.