{"id":"W4226354324","doi":"10.3390/jrfm15040165","title":"Time to Assess Bias in Machine Learning Models for Credit Decisions","year":2022,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Law, AI, and Intellectual Property","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Underwriting; Loan; Analytics; Compliance (psychology); Actuarial science; Credit score; Computer science; Finance; Business; Artificial intelligence; Data science; Psychology","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":[0.0501642,0.001304989,0.002076619,0.001798046,0.00151528,0.004351899,0.003118631,0.002848598,0.01160625],"category_scores_gemma":[0.1959924,0.000756124,0.001686304,0.002010027,0.002289184,0.007173447,0.003640501,0.007214243,0.001434847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00275432,"about_ca_system_score_gemma":0.004490591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01052611,"about_ca_topic_score_gemma":0.01053642,"domain_scores_codex":[0.9835228,0.01265967,0.000628084,0.001270183,0.001433655,0.0004855124],"domain_scores_gemma":[0.7567292,0.2295334,0.003153612,0.005395674,0.003928556,0.001259439],"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":[0.0007451831,0.0001844732,0.01762406,0.0004091415,0.0005171612,0.0006205357,0.0007238584,0.3935768,0.0003927652,0.4116611,0.01062637,0.1629184],"study_design_scores_gemma":[0.00004674745,0.00008922566,0.0007312545,0.00006703458,0.00004280759,0.00007788221,0.00008402752,0.8294,0.0002527483,0.1656799,0.003503547,0.00002477275],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02746777,0.002520534,0.9506894,0.009396833,0.0008521213,0.0001646084,0.000345343,0.0006937211,0.007869646],"genre_scores_gemma":[0.7669466,0.002353125,0.2043789,0.002030009,0.001461071,0.000895703,0.001274616,0.0006414824,0.02001852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0501642,"threshold_uncertainty_score":0.2652968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06786324974592264,"score_gpt":0.2570016938658317,"score_spread":0.1891384441199091,"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."}}