Example Concept: Organizations should adopt a multi-faceted approach to bias prevention, including the use of fairness dashboards to monitor bias metrics, regular audits to evaluate model performance across different demographic groups, and the implementation of bias mitigation techniques such as re-weighting, re-sampling, or adversarial debiasing. These practices help ensure that AI systems do not perpetuate or exacerbate existing inequalities.