{"id":"W2951512047","doi":"10.1017/s1744552319000077","title":"Fairness, accountability and transparency: notes on algorithmic decision-making in criminal justice","year":2019,"lang":"en","type":"article","venue":"International Journal of Law in Context","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Accountability; Transparency (behavior); Blame; Status quo; Big data; Criminal justice; Economic Justice; Optimism; Law and economics; Political science; Abstraction; Sociology; Law; Computer science; Psychology; Social psychology; Epistemology","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":[0.001887874,0.00008456714,0.0002088517,0.0001322392,0.00007757806,0.0001810168,0.0003874679,0.0001168055,0.0001388034],"category_scores_gemma":[0.001422834,0.00007603506,0.00006490889,0.00008101539,0.0001881455,0.0006835265,0.0000244175,0.0004382184,0.000008042995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002676046,"about_ca_system_score_gemma":0.0002417825,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004327453,"about_ca_topic_score_gemma":0.02927882,"domain_scores_codex":[0.9983767,0.0001318752,0.0004486429,0.0001273917,0.0007536773,0.0001617049],"domain_scores_gemma":[0.996639,0.002500433,0.0001974754,0.00005894767,0.0005436389,0.00006049766],"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":[0.0009407637,0.0004423942,0.05485115,0.00004297836,0.00006468609,0.0001890011,0.168053,0.00032939,0.0001470904,0.577376,0.00003791535,0.1975256],"study_design_scores_gemma":[0.007693223,0.0009310363,0.2346929,0.00622812,0.0001453709,0.00006905211,0.1278743,0.0010284,0.0001958595,0.5983515,0.02180783,0.0009824196],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9818831,0.0001930265,0.0002548405,0.004057035,0.001447315,0.00009977641,0.000007306154,0.000003712488,0.01205388],"genre_scores_gemma":[0.9978229,0.0002426425,0.000346702,0.001286006,0.0002817882,8.842135e-7,3.141739e-7,0.000005564094,0.00001317131],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1965432,"threshold_uncertainty_score":0.9884343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05764786785636324,"score_gpt":0.4215282451482901,"score_spread":0.3638803772919269,"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."}}