Example Concept: Algorithmic bias prevention involves establishing a governance framework that includes bias detection and mitigation strategies, such as pre-processing data to remove bias, in-processing techniques to ensure fairness during model training, and post-processing adjustments to correct biased outcomes. Organizations should also regularly audit their models using fairness dashboards and maintain transparency through model cards that document the model's purpose, limitations, and performance across different demographic groups.