{"id":"W4403444212","doi":"10.48550/arxiv.2410.08407","title":"What is Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Alberta Innovates","keywords":"Distillation; Knowledge transfer; Economics; Computer science; Knowledge management; Chemistry; Chromatography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0001298845,0.0004030151,0.0003461759,0.0005587423,0.0001341714,0.001316025,0.0003962354,0.000418282,0.0002205658],"category_scores_gemma":[0.00002311316,0.0003940691,0.0001933761,0.0004190777,0.0002049401,0.00135364,0.001566507,0.0005286899,0.000265559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007352243,"about_ca_system_score_gemma":0.00004008652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001098059,"about_ca_topic_score_gemma":0.0004218895,"domain_scores_codex":[0.9986008,0.00001115007,0.0001388236,0.0008549198,0.00007067986,0.000323585],"domain_scores_gemma":[0.9992217,0.00003850183,0.00007483498,0.0004921763,0.0001458551,0.00002687214],"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":[0.0006787447,0.0005425731,0.2718456,0.01803599,0.001753766,0.002278059,0.004605559,0.003734661,0.0001437778,0.6431782,0.02726143,0.02594165],"study_design_scores_gemma":[0.002046565,0.00006111871,0.07078944,0.004405031,0.002938804,0.0000100749,0.00986866,0.1296146,0.0003123319,0.6094619,0.1661682,0.004323197],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887494,0.002046435,0.001311651,0.001833717,0.001058873,0.0003385138,0.00002884087,0.0006135569,0.004018945],"genre_scores_gemma":[0.9937475,0.001225674,0.000006819277,0.0002098058,0.0003456428,0.000001567589,0.00003331662,0.00004554061,0.004384081],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2010562,"threshold_uncertainty_score":0.9998511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06770843653422613,"score_gpt":0.1907450726109637,"score_spread":0.1230366360767376,"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."}}