This explores the ethicalEthical AI, privacy, and regulatory considerations surrounding the integration of AIArtificial Intelligence (AI) into genomicsGenomics. It highlights the challenges posed by the collapse of traditional anonymity in genomic dataGenomic data, the vulnerabilities of de-identification methods, and the rise of privacy-preserving machine learningMachine Learning (ML) (PPMLPrivacy-Preserving Machine Learning (PPML)) techniques such as federated learningFederated Learning (FL), homomorphic encryptionHomomorphic Encryption (HE), and differential privacyDifferential Privacy (DP). The chapter also addresses algorithmic biasAlgorithmic bias in genomic AIGenomic AI, emphasizing the need for diverse datasets and fairness auditingFairness auditing to ensure equitable precision medicinePrecision medicine. It delves into explainability and interpretability (XAIExplainable AI (XAI)) methods to address the “black box” problem in genomic AIGenomic AI, ensuring transparency and trust in clinical applications. Regulatory frameworksRegulatory frameworks, including the FDA’s SaMDFDA SaMD and the EU AI ActEU AI Act, are analyzed for their approaches to balancing innovation and compliance. The chapter concludes with discussions on global standardsGlobal standards for genomic dataGenomic data sharing, indigenous data sovereigntyIndigenous data sovereignty, and the ethical implications of emerging technologies like polygenic embryo screeningPolygenic embryo screening. It calls for collaboration across technical, ethicalEthical AI, and regulatory domains to ensure the responsible and inclusive development of genomic AIGenomic AI. ​