{"id":"W4394863499","doi":"10.2139/ssrn.4785927","title":"A Bayesian Approach to Discrimination-free Insurance Pricing","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Download; Bayesian probability; Computer science; World Wide Web; Internet privacy; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004478936,0.0001863526,0.0001915076,0.0004199929,0.0008576804,0.000495713,0.0008564821,0.00007944684,0.00001970361],"category_scores_gemma":[0.0001778552,0.0001717587,0.0001893776,0.001221362,0.0001237177,0.0005311537,0.00008574726,0.001270917,0.00005001449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001265161,"about_ca_system_score_gemma":0.001381333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001045183,"about_ca_topic_score_gemma":0.008532769,"domain_scores_codex":[0.9957702,0.0002487063,0.0003426224,0.0003816616,0.0008987044,0.002358093],"domain_scores_gemma":[0.9992814,0.0000569817,0.00008322663,0.0002987449,0.0001073081,0.0001723302],"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":[0.00001169988,0.00008720879,0.004975247,0.00003639315,0.0001509696,0.00001077574,0.008130357,0.0001560977,0.0000113128,0.9322617,0.001146164,0.0530221],"study_design_scores_gemma":[0.0005709686,0.0002172368,0.02668282,0.0001953098,0.0001288285,0.0001054459,0.03455905,0.0009943092,0.00001456421,0.8652465,0.07051994,0.0007650132],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2212985,0.01385647,0.3814025,0.01366269,0.003075677,0.001621017,0.00001975134,0.0006736753,0.3643897],"genre_scores_gemma":[0.9923086,0.002218218,0.0008827809,0.0002041982,0.0008446828,0.0000358103,0.00000248788,0.00003361692,0.003469634],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.77101,"threshold_uncertainty_score":0.7004117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01191736953041599,"score_gpt":0.2827233303036859,"score_spread":0.2708059607732699,"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."}}