{"id":"W3217791385","doi":"10.3390/jrfm14110565","title":"Creating Unbiased Machine Learning Models by Design","year":2021,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Machine learning; Credit card; Census; Demographics; Proxy (statistics); Artificial intelligence; Imperfect; Class (philosophy); World Wide Web; Population","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04172531,0.001320187,0.001299513,0.001077405,0.0007653164,0.003663745,0.002762254,0.002154989,0.006453311],"category_scores_gemma":[0.1036717,0.001321247,0.001451937,0.0007603919,0.002076851,0.003621102,0.003752658,0.003716211,0.002706457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001420103,"about_ca_system_score_gemma":0.003570842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007689793,"about_ca_topic_score_gemma":0.0009754253,"domain_scores_codex":[0.9646421,0.02704357,0.0011426,0.002870466,0.00365521,0.0006460117],"domain_scores_gemma":[0.9367034,0.04111372,0.002905147,0.01300597,0.005596285,0.0006754645],"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":[0.0007591847,0.0004788006,0.009637147,0.001044885,0.0006220168,0.0002861036,0.001187666,0.2822587,0.005946088,0.3919465,0.01233306,0.2935],"study_design_scores_gemma":[0.0003903474,0.0005081519,0.0007691672,0.0002413429,0.0001643009,0.0001458409,0.0001843443,0.6384395,0.00608877,0.3250997,0.02789887,0.00006969218],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002711282,0.00006571869,0.9946461,0.0003925665,0.00005768389,0.0004285393,0.00009674183,0.0004014556,0.001199922],"genre_scores_gemma":[0.1256695,0.0002610502,0.86559,0.0006919745,0.0001582394,0.00460585,0.000465215,0.0002943304,0.002263798],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04172531,"threshold_uncertainty_score":0.2206672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04074645205016847,"score_gpt":0.2989135114924633,"score_spread":0.2581670594422948,"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."}}