{"id":"W2937133415","doi":"10.1145/3313127","title":"That's not fair!","year":2019,"lang":"en","type":"article","venue":"XRDS Crossroads The ACM Magazine for Students","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Law and economics; Sociology","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","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002844934,0.0001904149,0.0002696794,0.00004377682,0.00154684,0.001194898,0.003785776,0.000210072,0.0002899731],"category_scores_gemma":[0.002793586,0.0001415222,0.0002313602,0.0002271725,0.0004928106,0.000510096,0.0008798541,0.0003208521,0.0008829252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000114766,"about_ca_system_score_gemma":0.0001930628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007840002,"about_ca_topic_score_gemma":0.001373538,"domain_scores_codex":[0.9973282,0.0001323825,0.0002336996,0.0003280388,0.00125577,0.0007219043],"domain_scores_gemma":[0.9976248,0.0006361557,0.0001569856,0.001077908,0.0003393123,0.0001648303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004320188,0.000744449,0.4797098,0.0001092253,0.0005110477,0.000008474538,0.1216668,0.00002298738,0.00260195,0.2034865,0.1799042,0.0108025],"study_design_scores_gemma":[0.00183184,0.0002646944,0.2108334,0.0000386818,0.00006446711,6.875366e-7,0.006831533,0.000011109,0.000319095,0.06921501,0.7101174,0.0004720106],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.903748,0.00007060623,0.00003824984,0.03877674,0.001812663,0.001288502,0.0000485936,0.0001250291,0.05409158],"genre_scores_gemma":[0.9376,0.0001106874,0.000318688,0.003242798,0.0005514728,0.00004917147,0.000008628332,0.00003287275,0.05808569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5302132,"threshold_uncertainty_score":0.999895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06921837847880936,"score_gpt":0.4494009140248029,"score_spread":0.3801825355459935,"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."}}