The rapid integration of Artificial Intelligence (AI) into social work practice has introduced both significant opportunities and complex ethical challenges. This study examines the ethical implications of AI in social work practice, focusing on its role in risk assessment, client data management, service planning, predictive analytics, and communication systems. While AI technologies enhance efficiency, improve case management, and support evidence-based decision-making, their use raises critical concerns related to client confidentiality, privacy, algorithmic bias, accountability, and the preservation of professional values. The study explores how AI systems rely on large volumes of sensitive client data, increasing the risks of data breaches, unauthorized access, and misuse of confidential information. It also analyzes the potential for algorithmic bias arising from historically unequal datasets, which may result in discriminatory outcomes that contradict the profession’s commitment to social justice and equity. Furthermore, the research highlights concerns regarding professional accountability in AI-assisted decision-making and the possible reduction of human interaction and empathy in client relationships. Using a conceptual and literature-based approach, the study reviews existing scholarly works to assess the alignment of AI applications with core social work principles. The study indicate that while AI can strengthen service delivery and reduce administrative burdens, its ethical use requires clear guidelines, strong data protection measures, ongoing professional training, and consistent human oversight. The study concludes that AI should function as a supportive tool rather than a substitute for professional judgment and human-centered practice. Ensuring responsible implementation is essential to safeguarding clients’ rights, maintaining trust, and upholding the ethical foundations of the social work profession.
Key words: Artificial intelligence, social work practice, ethics, confidentiality, professional accountability and data privacy.
