{"id":"W4406354994","doi":"10.1109/gcaiot63427.2024.10833589","title":"Decoding Justice: The Synergy of Artificial Intelligence and Machine Learning in the Legal Landscape","year":2024,"lang":"en","type":"article","venue":"","topic":"Digital Transformation in Law","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Decoding methods; Computer science; Artificial intelligence; Economic Justice; Machine learning; Telecommunications; Political science; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006164298,0.00005271558,0.00009218696,0.000093972,0.00005149439,0.0002113308,0.0001273264,0.00002338453,0.0001725349],"category_scores_gemma":[0.00006947685,0.0000338348,0.00002923678,0.0002435227,0.0000489237,0.0002772981,0.00001863404,0.000136852,0.00005920667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007053412,"about_ca_system_score_gemma":0.000005822701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008208899,"about_ca_topic_score_gemma":0.00009322805,"domain_scores_codex":[0.9994306,0.00001017387,0.0003366111,0.0001048546,0.00002557083,0.00009221865],"domain_scores_gemma":[0.9996324,0.0002351016,0.00003875536,0.00007745634,0.000005773405,0.00001053375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002644975,0.000008262579,0.0007143919,0.00004103318,0.00000612436,0.000001208741,0.0007560457,0.00122562,0.000001535542,0.9835864,0.0000419823,0.01361471],"study_design_scores_gemma":[0.00006781209,0.00007570867,0.001564978,0.00005806474,0.0000140193,0.00002539676,0.003468364,0.7584729,0.0002484348,0.1407777,0.09501851,0.0002080454],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1352729,0.005958607,0.1283009,0.005968923,0.0006971953,0.0002443521,0.00005203696,0.00007568046,0.7234294],"genre_scores_gemma":[0.9992117,0.0002139665,0.0001077987,0.0001080093,0.00003192462,0.000005581243,0.000003072291,0.000005178451,0.0003127894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8639387,"threshold_uncertainty_score":0.2037868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05277570252949694,"score_gpt":0.2518822788851309,"score_spread":0.199106576355634,"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."}}