{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","open_science"],"consensus_categories":["sts","open_science"],"category_scores_codex":[0.004081413,0.00002769086,0.00005373185,0.00002432536,0.006986666,0.00006853251,0.09441461,0.00001680649,0.00004801547],"category_scores_gemma":[0.02585364,0.0000240498,0.00002631944,0.0006258564,0.003791964,0.0004528693,0.09205174,0.0002614342,0.000003380501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007709596,"about_ca_system_score_gemma":0.0006124147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006273582,"about_ca_topic_score_gemma":0.006358445,"domain_scores_codex":[0.9987808,0.0003902219,0.0001054412,0.0000951337,0.0004989556,0.0001294232],"domain_scores_gemma":[0.9671858,0.0004837093,0.0001088299,0.03198645,0.0001909289,0.00004426573],"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":[5.463717e-7,0.00004342472,0.0001284082,5.992118e-7,0.000003447494,1.550313e-8,0.006476225,0.000004257712,0.0002717623,0.9822407,0.01056109,0.0002694961],"study_design_scores_gemma":[0.00003673929,0.000006025721,0.0004893615,0.000003746265,0.000007541777,1.497282e-7,0.007234612,0.00003076151,0.00004931532,0.3337803,0.6583219,0.00003950637],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01031492,0.000482129,4.614604e-7,0.644129,0.0002148351,0.0001400057,0.00009056678,0.00002245335,0.3446057],"genre_scores_gemma":[0.992883,0.0002749157,0.005382073,0.001163469,0.00001755026,0.000006313172,0.000006107151,0.000003080159,0.0002634813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9825681,"threshold_uncertainty_score":0.9989191,"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."}}