{"id":"W4391759841","doi":"10.2139/ssrn.4709243","title":"A Fair price to pay: exploiting causal graphs for fairness in insurance","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Qualitative Comparative Analysis Research","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal; Université Laval","funders":"","keywords":"Actuarial science; Business; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009003075,0.0001367241,0.000250142,0.000552042,0.0005093617,0.0002983493,0.0004387397,0.00005897566,0.00006138588],"category_scores_gemma":[0.001071107,0.0001107778,0.0001670197,0.001996224,0.0001201838,0.0005102203,0.00004047541,0.001450446,0.00008455335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001774429,"about_ca_system_score_gemma":0.003975994,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008467952,"about_ca_topic_score_gemma":0.02692632,"domain_scores_codex":[0.9953873,0.000777291,0.0003614116,0.0003275396,0.0006372868,0.002509238],"domain_scores_gemma":[0.9985595,0.0008134299,0.00006617657,0.00009763335,0.0003003558,0.0001628945],"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":[0.00004956994,0.00003889208,0.004085166,0.00001662783,0.0001173768,0.000008020234,0.01850796,0.00006279546,0.0005520243,0.9600509,0.0004420023,0.01606865],"study_design_scores_gemma":[0.0002988942,0.0002289178,0.002092016,0.0001077869,0.00001048933,0.000008507926,0.04555517,0.0003422559,0.000102046,0.9186478,0.03238035,0.0002257523],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4799626,0.003325282,0.5030516,0.008430921,0.0002836682,0.0006950669,0.0000103953,0.00008143271,0.004158966],"genre_scores_gemma":[0.9946913,0.001039571,0.0002100546,0.00007797455,0.0003199383,0.0001026954,0.000001800326,0.00002062865,0.003536073],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5147287,"threshold_uncertainty_score":0.9908298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05762633692636294,"score_gpt":0.4335453727231348,"score_spread":0.3759190357967718,"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."}}