ALGORITHMIC BIAS IN LAW: THE DISCRIMINATORY POTENTIAL AND LEGAL LIABILITY OF AI-BASED DECISION SUPPORT SYSTEMS
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.061 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.054 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".