{"id":"W4206918890","doi":"10.1145/3506575","title":"Data science meets law","year":2022,"lang":"en","type":"article","venue":"Communications of the ACM","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Data science; Engineering ethics; Artificial intelligence; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02816034,0.0007356702,0.001143027,0.002627698,0.006261449,0.01707662,0.001596486,0.01143582,0.02618603],"category_scores_gemma":[0.0705668,0.0008651002,0.0007185165,0.00209374,0.02790896,0.02785955,0.01081441,0.01329844,0.01004022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005236772,"about_ca_system_score_gemma":0.01775201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004515834,"about_ca_topic_score_gemma":0.004515951,"domain_scores_codex":[0.9760516,0.01285559,0.001272887,0.002558253,0.006244299,0.001017354],"domain_scores_gemma":[0.9420819,0.03597676,0.00221205,0.01030314,0.005416192,0.004009953],"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":[0.00001688083,0.00002252103,0.0003376862,0.0001315693,0.00001572899,0.00006530251,0.0009461786,0.0002532341,0.0001150635,0.9155522,0.05001212,0.03253156],"study_design_scores_gemma":[0.000007192263,0.0000104438,0.0001037626,0.0001783304,0.000005619403,0.00005849229,0.0006371527,0.0004460223,0.0001754505,0.733902,0.2644655,0.00001005318],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.002397493,0.01568813,0.107192,0.5648596,0.005688206,0.0001023187,0.0002682353,0.0006211468,0.3031827],"genre_scores_gemma":[0.4837526,0.02707237,0.124752,0.1441936,0.01524118,0.0007121645,0.0006893732,0.001370683,0.202216],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02816034,"threshold_uncertainty_score":0.1489279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3725016348255044,"score_gpt":0.4984871662198992,"score_spread":0.1259855313943948,"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."}}