{"id":"W2767613032","doi":"10.3138/utlj.2017-0052","title":"How artificial intelligence will affect the practice of law","year":2018,"lang":"en","type":"article","venue":"University of Toronto Law Journal","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":164,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Practice of law; Transparency (behavior); Economic Justice; Work (physics); Legal profession; Affect (linguistics); Public relations; Law; Software; Computer science; Knowledge management; Business; Political science; Sociology; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.01451111,0.0005248995,0.0005101071,0.001662453,0.008657804,0.01595208,0.00127863,0.008618664,0.007658479],"category_scores_gemma":[0.01962851,0.0004771818,0.0005179002,0.001240258,0.0645557,0.01364416,0.00406228,0.006843807,0.001585697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02378616,"about_ca_system_score_gemma":0.01836549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08189308,"about_ca_topic_score_gemma":0.05517722,"domain_scores_codex":[0.9874178,0.007021187,0.0002310154,0.001293343,0.002431579,0.001605045],"domain_scores_gemma":[0.9869831,0.006524222,0.0006906182,0.001807296,0.002198856,0.001795819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001132702,0.00003077251,0.0008629799,0.00004419027,0.00001411372,0.00008814684,0.003818502,0.0006431015,0.00009071572,0.9725094,0.01355351,0.008333221],"study_design_scores_gemma":[0.00002200023,0.00002252346,0.001162304,0.0002140017,0.00001246,0.00006464058,0.002898971,0.0006438875,0.0001905072,0.7212259,0.2735157,0.00002714317],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01992446,0.02289184,0.01381738,0.5202686,0.002631633,0.00008359303,0.0001589846,0.0001322186,0.4200913],"genre_scores_gemma":[0.8688064,0.0188088,0.01258674,0.05038806,0.002662986,0.0001305055,0.0000872576,0.0001426125,0.04638659],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08189308,"threshold_uncertainty_score":0.1725815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04278348900904203,"score_gpt":0.321486638661484,"score_spread":0.2787031496524419,"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."}}