{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00214207,0.00007817953,0.0001901986,0.00005820569,0.0008493144,0.0001976185,0.0001069149,0.00008207693,0.0000253043],"category_scores_gemma":[0.001110676,0.00007385495,0.000077719,0.0001951732,0.00008299184,0.0003302166,0.00004758022,0.0003957962,0.000001133635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000516288,"about_ca_system_score_gemma":0.0001287794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006221132,"about_ca_topic_score_gemma":0.0002249335,"domain_scores_codex":[0.9987016,0.0003520918,0.0002588943,0.00009880222,0.00038183,0.0002068127],"domain_scores_gemma":[0.9990638,0.0002382853,0.0002650873,0.00004761583,0.0002558377,0.000129358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002055097,0.0003746182,0.006193216,0.00007222349,0.0001410988,0.000614876,0.07715931,0.006670309,0.0001529484,0.2493188,0.009512898,0.6495842],"study_design_scores_gemma":[0.0028555,0.0005511764,0.004826461,0.0003882396,0.0004327889,0.00001124287,0.02913229,0.003092045,0.0002520741,0.3644448,0.5933582,0.0006552445],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1478032,0.01319974,0.756117,0.005370619,0.001006261,0.0003647756,0.00002136529,0.00005298411,0.07606399],"genre_scores_gemma":[0.9627761,0.0279236,0.007353646,0.0003378517,0.0002796382,9.393496e-7,0.000001231129,0.00000873881,0.00131831],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8149728,"threshold_uncertainty_score":0.6532326,"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."}}