{"id":"W7127287883","doi":"10.38159/ehass.202561512","title":"Algorithmic Justice in South Africa: Safeguarding Human Rights in AI-Driven Legal Systems","year":2025,"lang":"en","type":"article","venue":"E-Journal of Humanities Arts and Social Sciences","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Human rights; Safeguarding; Statutory law; Enforcement; Compromise; Economic Justice; Fundamental rights; Normative","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003530368,0.0001322047,0.0003909982,0.0004817213,0.004006536,0.001147499,0.0004161347,0.0001495082,0.00002994367],"category_scores_gemma":[0.0001529215,0.0001146229,0.00008951834,0.0004513553,0.001666311,0.001164357,0.00006453414,0.0004918565,0.000001192289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002111864,"about_ca_system_score_gemma":0.000431226,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006586034,"about_ca_topic_score_gemma":0.01808014,"domain_scores_codex":[0.9977831,0.0003211909,0.0005472582,0.0001807364,0.0006851669,0.0004825373],"domain_scores_gemma":[0.9991171,0.0002279028,0.0002855623,0.00004272063,0.000266528,0.00006021994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.000005978471,0.00003567439,0.0006801706,0.00002436274,0.0000119356,0.00003176089,0.2028275,0.00002829536,0.00001384683,0.7954816,0.0007775264,0.00008140923],"study_design_scores_gemma":[0.001141245,0.0003025745,0.006397658,0.0006299142,0.00009188981,0.000003876358,0.6461747,0.0001120575,0.00000819544,0.1121997,0.2324577,0.0004804902],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7147782,0.0008642491,0.00002510061,0.004783306,0.001590868,0.0002179186,0.000005602228,0.00001733639,0.2777175],"genre_scores_gemma":[0.9971273,0.00004124003,0.00004330763,0.0002659113,0.0007856531,0.000002147428,2.579131e-7,0.000003640187,0.001730576],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6832819,"threshold_uncertainty_score":0.9998894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08334877776149278,"score_gpt":0.3697697990927314,"score_spread":0.2864210213312386,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}