{"id":"W7125213011","doi":"10.25949/31093552","title":"Fairness Evaluation and Inference Level Mitigation in LLMs","year":2025,"lang":"","type":"dissertation","venue":"Open MIND","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Politics; Ideology; Ethnic group; Inference; Salient; Cultural bias; Cultural diversity; Convergence (economics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005659683,0.0002987417,0.0005541907,0.0004741211,0.0005589431,0.0008721051,0.0006182457,0.0003832256,0.004147102],"category_scores_gemma":[0.001709475,0.0003291833,0.00009562274,0.001686563,0.0001239324,0.0007392314,0.0001665188,0.0003103075,0.00007081726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002214437,"about_ca_system_score_gemma":0.00248875,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006055336,"about_ca_topic_score_gemma":0.06469432,"domain_scores_codex":[0.9955109,0.001551239,0.0008033642,0.000864509,0.000987883,0.0002820931],"domain_scores_gemma":[0.9971706,0.001095651,0.0005219647,0.0001894362,0.0009156205,0.0001067634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00004550703,0.00009429029,0.003997443,0.00003216462,0.00005564887,0.000001388536,0.02642244,0.0008216028,0.00008210583,0.001197092,0.00001016355,0.9672402],"study_design_scores_gemma":[0.00310712,0.0001306467,0.5811416,0.002548944,0.001440168,8.906928e-7,0.0887996,0.2142481,0.001256953,0.09547404,0.01013046,0.001721515],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9074939,0.0004456138,0.002256438,0.0004680445,0.0006817919,0.001733061,0.00003265486,0.000001198534,0.08688734],"genre_scores_gemma":[0.9701939,0.000178507,0.008020533,0.000022626,0.0000855806,0.0001272256,0.001139878,0.00000840398,0.02022335],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9655187,"threshold_uncertainty_score":0.999916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2085280169993381,"score_gpt":0.5219760850522652,"score_spread":0.3134480680529271,"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."}}