{"id":"W4415743759","doi":"10.1215/10539867-11834158","title":"The Implications of AI for Criminal Justice","year":2025,"lang":"en","type":"article","venue":"Federal Sentencing Reporter","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Criminal justice; Key (lock); Stakeholder; Economic Justice; Criminal behaviour","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0009797221,0.00005134708,0.00009448907,0.00002228645,0.002173461,0.0001811606,0.0001495848,0.0000735724,0.000006630035],"category_scores_gemma":[0.002275154,0.00003853796,0.00009600959,0.0001125999,0.0002188673,0.0001135874,0.00002542586,0.0001072321,7.233488e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006295511,"about_ca_system_score_gemma":0.0004750128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003978553,"about_ca_topic_score_gemma":0.008528586,"domain_scores_codex":[0.99919,0.0000445886,0.000264979,0.0001153644,0.0001619879,0.0002230182],"domain_scores_gemma":[0.9984855,0.0005065129,0.0001329429,0.000151456,0.0006766423,0.00004696046],"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":[0.00003783459,0.00006800272,0.01193195,0.0001186792,0.00009758001,0.000002385611,0.01991078,0.000006172416,0.001729597,0.8770168,0.07347342,0.01560673],"study_design_scores_gemma":[0.0005051736,0.0001143363,0.05890377,0.0001655492,0.0005563883,0.000005256343,0.1165771,0.0001110578,0.001344298,0.5226747,0.2987137,0.0003286159],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.3276926,0.0002462181,0.01850863,0.3772007,0.002594807,0.001151287,0.00001526112,0.0001143954,0.2724761],"genre_scores_gemma":[0.9899085,0.00003372725,0.0002889228,0.003035462,0.0001965369,0.00002236225,0.000002045247,0.000004158343,0.006508339],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6622158,"threshold_uncertainty_score":0.9991256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05661944826564597,"score_gpt":0.4215016244764811,"score_spread":0.3648821762108351,"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."}}