{"id":"W4213014074","doi":"10.21203/rs.3.rs-1356281/v1","title":"Dbias: Detecting biases and ensuring Fairness in news articles","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Computer science; Internet privacy; Business","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":[0.04918593,0.001163238,0.002157951,0.004300782,0.002901618,0.007235161,0.00284935,0.003176372,0.005227693],"category_scores_gemma":[0.2009933,0.001203742,0.0006910909,0.002507244,0.003188016,0.00703678,0.00702392,0.002696283,0.002279551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001950432,"about_ca_system_score_gemma":0.004793063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003048144,"about_ca_topic_score_gemma":0.003163393,"domain_scores_codex":[0.9576596,0.02166269,0.002953017,0.006997419,0.008754943,0.001972298],"domain_scores_gemma":[0.8005377,0.140902,0.01155099,0.02849727,0.01384476,0.004667273],"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":[0.006049699,0.001015902,0.1477626,0.001442913,0.0008193072,0.0003790745,0.004683031,0.02511156,0.02322967,0.08369745,0.0381384,0.6676703],"study_design_scores_gemma":[0.0008358016,0.0007936738,0.02747539,0.0002670529,0.0003726045,0.0004500537,0.002524603,0.6155142,0.06719083,0.2554971,0.02885782,0.000220895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2399154,0.001485839,0.7193474,0.003765203,0.000885537,0.0009065481,0.004014208,0.01724058,0.01243925],"genre_scores_gemma":[0.7502842,0.0002099477,0.2412599,0.0005574372,0.0003729224,0.0004994093,0.001733953,0.0007185776,0.004363674],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04918593,"threshold_uncertainty_score":0.2601232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1149649579783896,"score_gpt":0.3766143034695302,"score_spread":0.2616493454911407,"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."}}