{"id":"W4406480489","doi":"10.1111/jori.12503","title":"A fair price to pay: Exploiting causal graphs for fairness in insurance","year":2025,"lang":"en","type":"article","venue":"Journal of Risk & Insurance","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Université Laval","funders":"","keywords":"Actuarial science; Economics; Business","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":[0.003681575,0.000144445,0.0004209846,0.0003107238,0.000579586,0.0001886894,0.0005052992,0.0001807963,0.000004829287],"category_scores_gemma":[0.00688431,0.0001229952,0.0001996495,0.001169304,0.0001636628,0.0007642402,0.00003534407,0.0006949217,0.000002459934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002547571,"about_ca_system_score_gemma":0.0006717971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002433269,"about_ca_topic_score_gemma":0.005023145,"domain_scores_codex":[0.9978663,0.0003016772,0.0006568557,0.0001861098,0.0005169524,0.0004720912],"domain_scores_gemma":[0.9969096,0.001230297,0.0005387875,0.0001328672,0.0009969487,0.0001915611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004526204,0.0002649537,0.7294646,0.0001082266,0.0000929059,0.00003816228,0.06161155,0.0006015901,0.0004650506,0.1616153,0.002490686,0.04279437],"study_design_scores_gemma":[0.001448219,0.0001606206,0.7363554,0.0007892295,0.00001832328,0.000002033661,0.009630406,0.00001385421,0.0002414714,0.202111,0.04893998,0.0002894203],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9771774,0.0005072576,0.007999181,0.009978266,0.001115063,0.0003556339,0.0000373007,0.0000226886,0.002807222],"genre_scores_gemma":[0.9949358,0.001573343,0.001913031,0.001065659,0.0003239411,0.00001627601,3.285486e-7,0.00001312652,0.0001584879],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05198114,"threshold_uncertainty_score":0.824166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02616377803767753,"score_gpt":0.3645533110817164,"score_spread":0.3383895330440388,"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."}}