Algorithmic Bias in Law: The Discriminatory Potential and Legal Liability of AI-Based Decision Support Systems

This study examines the impact of artificial intelligence (AI)-based algorithmic decision-making systems on human rights through a multidimensional legal and empirical approach. Specifically, it evaluates the structural inequalities resulting from algorithmic bias in critical sectors such as criminal justice, social rights, public services, and private sector operations. Through content analysis and comparative case studies, the article investigates a range of international examples— including the COMPAS and PredPol systems in the United States, the SyRI and Ofqual algorithms in Europe, and immigration and welfare tools deployed in countries like Canada and Australia. The article is structured into four main sections. First, it explores how algorithmic systems operate based on biased datasets and the implications of such processes for marginalized social groups. The second section discusses how algorithmic tools have contributed to the reproduction of inequality in public service delivery. The third section analyzes how AI technologies used in education, healthcare, and immigration procedures may yield outcomes that conflict with fundamental human rights. Lastly, the article focuses on digital discrimination in the private sector and the emerging threats to consumer protection and equality. The study argues that algorithmic justice is not merely a technical challenge but also an ethical, legal, and institutional one. In its concluding section, the article proposes holistic solutions such as fair machine learning practices, principles of algorithmic transparency, mandatory ethical impact assessments, and the establishment of independent oversight bodies. The findings underscore the need for a multidisciplinary, normatively grounded, and transparent governance framework to ensure that algorithmic systems are designed and implemented in accordance with international human rights standards.