Example Concept: Developers should implement fairness-aware machine learning practices, such as using pre-processing techniques to balance datasets, in-processing methods to adjust model training, and post-processing strategies to correct biased outputs. They should also utilize fairness dashboards to monitor and evaluate model performance across different demographic groups.