{"id":"W4415230053","doi":"10.1609/aies.v8i2.36616","title":"From Efficiency to Equity: Measuring Fairness in Preference Learning","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI/ACM Conference on AI Ethics and Society","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Preference; Preference learning; Generative grammar; Preference elicitation; Inequity aversion; Inequality; Work (physics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02908724,0.000947657,0.001315359,0.003535344,0.001347955,0.004900004,0.001689863,0.001614929,0.002253607],"category_scores_gemma":[0.2015333,0.0003181515,0.0009848957,0.003558484,0.003626386,0.00805978,0.005055751,0.002565614,0.0003246082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002308862,"about_ca_system_score_gemma":0.001544594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002658875,"about_ca_topic_score_gemma":0.002180249,"domain_scores_codex":[0.9731936,0.0161116,0.001596518,0.002688777,0.005673822,0.0007357636],"domain_scores_gemma":[0.8500041,0.1169497,0.008886595,0.01574638,0.006624855,0.001788371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001736622,0.0006752328,0.1343052,0.0006280635,0.0008888008,0.0002144952,0.004968432,0.2354444,0.004231954,0.2564716,0.003561082,0.3568741],"study_design_scores_gemma":[0.00006347671,0.000376866,0.01965054,0.0001589883,0.0001189052,0.0002183805,0.001021278,0.5101063,0.005700048,0.4595766,0.002873296,0.0001354104],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2521438,0.0007013451,0.7367169,0.000997305,0.0000739414,0.0002218982,0.0005202055,0.0002718216,0.008352873],"genre_scores_gemma":[0.9423465,0.0001290634,0.05628734,0.0001510874,0.00004461614,0.0001366597,0.0003364974,0.0000643105,0.0005039208],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02908724,"threshold_uncertainty_score":0.1538299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2746527999111085,"score_gpt":0.3042662474551201,"score_spread":0.02961344754401157,"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."}}