Researchers introduced ε-iFair, a job recommendation algorithm that adds tunable individual fairness guarantees while keeping recommendations calibrated to qualifications and historical interactions. The work is positioned for hiring and placement use cases where bias constraints must be enforced without breaking statistical calibration. A separate framework, which combines differential privacy with explainable auditing, reports high-performing network-attack detection while meeting legal transparency requirements—using a compliance-oriented SHAP-based process to flag how model decisions map to required explanations. While both studies sit outside classic drug development, they directly affect biotech operations where AI is increasingly used for risk screening, recruitment, and data access governance in clinical and administrative workflows.