{"id":"W4385291841","doi":"10.48550/arxiv.2307.13081","title":"Fairness Under Demographic Scarce Regime","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Classifier (UML); Proxy (statistics); Scarcity; Fairness measure; Data mining; Machine learning; Artificial intelligence; Microeconomics; Economics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001481001,0.000499539,0.0005561974,0.001004728,0.001001686,0.0002395688,0.00208004,0.0006726335,0.0001503933],"category_scores_gemma":[0.00007555525,0.0006364468,0.0007029083,0.002642625,0.001242829,0.00033691,0.00144802,0.000923339,0.0005010301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003823926,"about_ca_system_score_gemma":0.0003375047,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01139881,"about_ca_topic_score_gemma":0.01429373,"domain_scores_codex":[0.9958804,0.0007180343,0.000348646,0.001627677,0.0004511682,0.000974095],"domain_scores_gemma":[0.9973143,0.0001963168,0.0004144809,0.00141166,0.0003184374,0.000344792],"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":[0.00005112587,0.0001806442,0.194006,0.0002122409,0.000633436,0.0005512529,0.001586343,0.04340693,0.000003031347,0.7546006,0.004423672,0.0003446309],"study_design_scores_gemma":[0.001121687,0.00005745396,0.2988704,0.0004189196,0.0009110262,0.00000120075,0.013099,0.0042879,0.0000102211,0.6538247,0.02504929,0.002348155],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9009229,0.0002159143,0.02194393,0.001284273,0.004035785,0.001618559,0.00008018155,0.001897132,0.06800131],"genre_scores_gemma":[0.9791251,0.002630449,0.00007032834,0.0001807263,0.0003509373,0.000006367817,0.00004539026,0.00007342849,0.01751732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1048644,"threshold_uncertainty_score":0.9996087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1311969628857901,"score_gpt":0.2406693803141218,"score_spread":0.1094724174283317,"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."}}