Artificial Intelligence in Child Welfare Decision-Making: Ethical Frameworks and Practice Implications

Abstract

The rapid integration of artificial intelligence (AI) into child welfare systems has transformed how risk is assessed, resources are allocated, and decisions are made about vulnerable families. This paper examines the ethical frameworks and practice implications surrounding the deployment of predictive analytics, algorithmic risk-assessment instruments, and machine-learning decision-support tools in statutory child protection services. Through a comprehensive literature review and theoretical synthesis, this study investigates the intersection of social work values, algorithmic governance, and the lived realities of children and families who become objects of computational scrutiny. Findings indicate that while AI tools promise improved consistency, earlier identification of risk, and more efficient use of scarce caseworker time, they simultaneously raise serious concerns regarding algorithmic bias, due process, transparency, and the erosion of professional discretion. The paper proposes a seven-step ethical decision-making framework specific to AI-augmented child welfare practice and identifies critical practice strategies including human-in-the-loop case review, family-engaged algorithmic literacy, bias-auditing protocols, and structured rights-based explanation procedures. Implications for social work education, agency policy, and regulatory reform are discussed, with particular emphasis on the urgent need for algorithmic accountability standards and culturally responsive design that aligns with the profession’s commitment to social justice.