{"id":"W7093316595","doi":"10.25108/2304-1730-1749.iolr.2025.80.56-64","title":"Artificial Intelligence as a Judge: Myth of Impartiality and Legal Risks of Judicial Automation","year":2025,"lang":"","type":"article","venue":"Juridical Sciences and Education","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Impartiality; Automation; Human rights; Dual (grammatical number); Mythology; Key (lock)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05889659,0.0003948055,0.0006954415,0.003353925,0.007632694,0.01931223,0.002582213,0.007100176,0.001425508],"category_scores_gemma":[0.09485863,0.0004113422,0.0007786152,0.001863255,0.0866871,0.02002576,0.006642066,0.008985607,0.0003197311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005490725,"about_ca_system_score_gemma":0.007639646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002889683,"about_ca_topic_score_gemma":0.00151728,"domain_scores_codex":[0.9417306,0.03620286,0.002429229,0.004284981,0.01332969,0.002022675],"domain_scores_gemma":[0.8905302,0.08625329,0.007260299,0.008543175,0.006190019,0.001222952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002110068,0.0000167335,0.001208387,0.00004548542,0.00001302472,0.00005593543,0.007498314,0.0009386314,0.00007552159,0.9780849,0.001321115,0.01072073],"study_design_scores_gemma":[0.00001455932,0.00003041486,0.001083063,0.0003655292,0.00001506222,0.0001154945,0.003698681,0.002040094,0.0002859031,0.9709969,0.02132221,0.00003212039],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1928243,0.01777389,0.09333771,0.4189105,0.001249255,0.0001205936,0.00009714059,0.0001322582,0.2755544],"genre_scores_gemma":[0.9843412,0.001863066,0.006423557,0.00519817,0.0005179784,0.00006806338,0.00001441379,0.00002504657,0.001548456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05889659,"threshold_uncertainty_score":0.3114786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1099702623304596,"score_gpt":0.4714636627226123,"score_spread":0.3614934003921527,"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."}}