AAISM identifies fairness constraints (e.g., constrained optimization, debiasing objectives, conditional generation controls, and post-processing calibrations) as the most direct, measurable method to mitigate disparate outcomes in generative systems. While data augmentation can help with coverage, and adversarial training improves robustness, fairness constraints explicitly target distributional fairness and outcome equity in generated content, aligning with governance and compliance goals.