AI Bias as a Human Rights Violation: Legal Standards and Judicial Remedies in Automated Decision Systems

Abstract

The rapid integration of automated decision-making systems into public and private governance has intensified global concern over the human rights implications of algorithmic bias. As machine learning tools increasingly shape outcomes in criminal justice, welfare administration, migration control, employment screening, healthcare triage, and financial services, evidence shows that these systems often reproduce and scale structural inequalities embedded within historical data and institutional practices. This narrative review synthesizes current scholarship to analyze how biased algorithms undermine core human rights principles, including equality, non-discrimination, due process, transparency, privacy, and freedom from arbitrary decision-making. The article examines the conceptual foundations of AI-driven discrimination, highlighting how technical, societal, and structural biases interact during data collection, model development, and deployment. It then evaluates international, regional, and sector-specific legal frameworks governing automated decision systems, identifying significant gaps and inconsistencies that hinder effective accountability. Judicial approaches and case law are assessed to illustrate both the potential and limitations of litigation as a mechanism for addressing algorithmic harm. The review also explores existing and emerging remedies—such as injunctions, algorithmic audits, impact assessments, algorithmic affirmative action, and mandated transparency—and considers the challenges courts face in regulating opaque and technically complex systems. Finally, the article outlines governance models that integrate state responsibility, corporate due diligence, civil society participation, and international norm-setting, emphasizing the importance of preventive, lifecycle-based regulation over reactive judicial intervention. The findings underscore the urgent need for harmonized, rights-based governance structures capable of mitigating discriminatory outcomes and ensuring that automated decision systems operate in alignment with democratic values and human dignity.