{"id":"W4403640394","doi":"10.1007/978-3-031-71975-2_6","title":": Mitigating Gender Bias in Neural Team Recommendation via Female-Advocate Loss Regularization","year":2024,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Regularization (linguistics); Computer science; Gender bias; Medicine; Psychology; Artificial intelligence; Social psychology","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":[0.003050244,0.0008557371,0.0007975788,0.0004463711,0.0004716803,0.0005689432,0.001632884,0.001612392,0.004744984],"category_scores_gemma":[0.007166991,0.0003202183,0.0004808192,0.0004833544,0.000688489,0.001118567,0.001373874,0.001964506,0.001552469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006449156,"about_ca_system_score_gemma":0.0008506897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004362363,"about_ca_topic_score_gemma":0.007674378,"domain_scores_codex":[0.999235,0.0003785854,0.00002212758,0.000126253,0.0001615008,0.00007642295],"domain_scores_gemma":[0.9984516,0.0008298822,0.00009619382,0.0001968275,0.0003391241,0.00008639575],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007333426,0.0003817604,0.005452804,0.0001676717,0.000298273,0.0001222343,0.0002703746,0.2623387,0.005869127,0.04415452,0.06706332,0.6131479],"study_design_scores_gemma":[0.00002466154,0.00005187526,0.0003668276,0.00001830632,0.00002217081,0.00002470035,0.00002205205,0.9859719,0.001001389,0.01094626,0.001542751,0.000007207908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03830636,0.001805658,0.9480324,0.002701566,0.0005778568,0.00006879161,0.0003229949,0.0008507103,0.007333613],"genre_scores_gemma":[0.590629,0.0009279034,0.3474976,0.002165669,0.001195543,0.0002151055,0.001035025,0.0004897566,0.05584437],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004744984,"threshold_uncertainty_score":0.0161314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2762219647630983,"score_gpt":0.4254999534359285,"score_spread":0.1492779886728302,"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."}}