Example Concept: Developers should employ fairness evaluation techniques such as disparate impact analysis and fairness dashboards to identify biases in AI models. They are also responsible for selecting diverse and representative datasets, applying bias mitigation algorithms (e.g., re-weighting, adversarial debiasing), and documenting these efforts using frameworks like model cards to ensure transparency and accountability.