Algorithmic Justice in South Africa: Safeguarding Human Rights in AI-Driven Legal Systems
Bibliographic record
Abstract
This paper investigated the intersection between emerging algorithmic technologies and the enforcement of human rights within South Africa’s legal system. As artificial intelligence (AI) becomes increasingly integrated into legal processes ranging from predictive policing to judicial decision-making, urgent questions arise regarding the compatibility of such technologies with constitutional protections, ethical standards, and democratic accountability. The study critically examined how AI-driven legal tools may inadvertently entrench existing biases, obscure accountability, or compromise the right to a fair trial, especially for historically marginalised groups. This study drew on South Africa’s constitutional framework, global human rights principles, and a comparative analysis of AI regulation in the EU and Canada to assess how algorithmic systems can operate in a manner that respects and promotes justice, fairness, and transparency. The key focus areas of this study included the risk of data-driven discrimination, the opacity of algorithmic reasoning, and the adequacy of current regulatory safeguards. Through a detailed analysis of case law, journal articles, statutory developments, and technological trends, this study evaluates whether South Africa’s legal and institutional frameworks are sufficiently equipped to manage the risks and opportunities presented by AI. The paper offers normative and policy-oriented recommendations to ensure that algorithmic tools deployed within the legal domain uphold constitutional values, enhance legal accountability, and foster trust in the justice system. The study contributes by proposing a rights-based framework to ensure that AI in South Africa’s legal system upholds justice, accountability, and full regulation of AI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".