{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01975582,0.000778231,0.001474024,0.003179836,0.002477363,0.004628334,0.002616506,0.003338522,0.01140469],"category_scores_gemma":[0.1329472,0.001038877,0.001932499,0.003137653,0.008799967,0.01679947,0.004064588,0.003683346,0.0003521316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003132231,"about_ca_system_score_gemma":0.00290828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005265978,"about_ca_topic_score_gemma":0.004031124,"domain_scores_codex":[0.9866958,0.009719979,0.0004176508,0.001652194,0.001064292,0.0004501455],"domain_scores_gemma":[0.8304977,0.1499075,0.005192826,0.01083801,0.002101757,0.001462286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003859806,0.00003141941,0.0009854994,0.00005122232,0.00003093292,0.00005252326,0.000574332,0.01340024,0.00007283868,0.9761509,0.0005631337,0.008048506],"study_design_scores_gemma":[0.000008471808,0.000004317957,0.0001040743,0.00001003421,0.00001142539,0.00001332265,0.0000876659,0.02413864,0.00003862658,0.9751537,0.0004244651,0.000005223667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05184066,0.0002899471,0.9335232,0.003865029,0.00009954574,0.0001179917,0.0003585487,0.0001808627,0.009724181],"genre_scores_gemma":[0.8642863,0.0002182058,0.132875,0.0003203483,0.0001020858,0.0001816068,0.0001900493,0.0001082613,0.001718177],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01975582,"threshold_uncertainty_score":0.10448,"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."}}