{"id":"W4385571404","doi":"10.18653/v1/2023.acl-long.490","title":"BLIND: Bias Removal With No Demographics","year":2023,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Azrieli Foundation","keywords":"Demographics; Computer science; Task (project management); Artificial intelligence; Process (computing); Gender bias; Annotation; Machine learning; Debiasing; Psychology; Social psychology; Demography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01027315,0.003055936,0.002104427,0.002123531,0.001712703,0.002258692,0.003212455,0.003033098,0.003944931],"category_scores_gemma":[0.03613671,0.001163505,0.002544506,0.001182665,0.00175798,0.005006549,0.005334403,0.003709764,0.004990853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009087087,"about_ca_system_score_gemma":0.004185615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005501724,"about_ca_topic_score_gemma":0.008539327,"domain_scores_codex":[0.9949071,0.001789998,0.0002889412,0.001517137,0.0009867582,0.000510023],"domain_scores_gemma":[0.9865023,0.004948556,0.0009077009,0.004943502,0.002182375,0.0005154697],"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":[0.001493772,0.0007324761,0.03396949,0.0007870271,0.0008282123,0.0004534954,0.002123411,0.04851358,0.02981723,0.01838555,0.07361349,0.7892823],"study_design_scores_gemma":[0.0004703922,0.0006373441,0.01032516,0.0004565872,0.0005962646,0.001019439,0.0007333942,0.7877276,0.04698347,0.0890501,0.06165977,0.0003404445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04915105,0.001365472,0.9283847,0.001288454,0.0007580299,0.0005413817,0.001392494,0.01396338,0.003155121],"genre_scores_gemma":[0.4322615,0.0008477313,0.5376182,0.003644947,0.001179675,0.00110533,0.005209079,0.003514657,0.01461888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01027315,"threshold_uncertainty_score":0.05433023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07052993259609026,"score_gpt":0.2664610675017185,"score_spread":0.1959311349056282,"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."}}