Human Centered AI Ethics, Algorithmic Bias, and Data Synthesis in Social Work and Military Settings

Purpose: This study examines the ethical and professional implications of integrating Artificial Intelligence (AI) into clinical and community Social Work and its application within military settings, focusing on algorithmic bias, AI assisted data synthesis, privacy, and the preservation of human judgment. Research Methodology: The study adopts a qualitative documentary research design that draws on peer reviewed literature, professional standards, international policy documents, and relevant national frameworks. The documents were analyzed thematically through the lenses of Human Centered Design Theory, Sociotechnical Systems Theory, and the Social Justice Perspective. Results: AI offers opportunities to improve assessment, documentation, psychosocial intervention, research, and organizational decision making, but it raises serious concerns about privacy, confidentiality, informed consent, accountability, algorithmic bias, and transparency. These concerns intensify in military settings, where AI systems may process highly sensitive personal, health, and operational information about personnel, veterans, and their families. Conclusions: AI should be treated as a professional support technology rather than a substitute for Social Work judgment, therapeutic relationships, or accountable institutional decision making. Responsible adoption requires institutional safeguards, professional competencies, continuous auditing, meaningful human oversight, and mechanisms through which affected people can challenge AI supported decisions. Limitations: The study relies on secondary documents, does not test specific AI systems empirically, and focuses mainly on the Nigerian policy context. Contributions: The study proposes a Human Centered AI Framework for Social Work and Military Practice (HCAI SWMP) built on ethical governance, bias aware data management, secure data synthesis, and human centered implementation.