How would you address fairness and bias in the design of an AI system?

To address fairness and bias in an AI system, I would first conduct a thorough analysis of the training data to identify potential biases. This involves ensuring diverse and representative data collection to avoid underrepresentation of certain groups. During model training, I would implement techniques such as reweighting and adversarial debiasing to mitigate bias. Regular audits and evaluations of model outputs should be conducted to measure fairness metrics. Additionally, engaging a diverse team during the design process can provide multiple perspectives, which aids in identifying and correcting biases throughout the system's development lifecycle.