{"id":"W3115320986","doi":"10.1613/jair.1.12590","title":"Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective","year":2021,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Unintended consequences; Perspective (graphical); Human rights; Transparency (behavior); Situational ethics; The Internet; Redress; Social media; Situation awareness","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":[0.01014452,0.0003077023,0.0006665558,0.002721247,0.001379612,0.003552408,0.0006985885,0.001699576,0.002712995],"category_scores_gemma":[0.03662886,0.0004619748,0.0006635677,0.002413465,0.002576245,0.007821193,0.00304173,0.002820992,0.001018566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009411062,"about_ca_system_score_gemma":0.001635285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002685281,"about_ca_topic_score_gemma":0.003046143,"domain_scores_codex":[0.9903373,0.00491744,0.001056794,0.0005404059,0.002257612,0.0008904428],"domain_scores_gemma":[0.9653189,0.02064037,0.006531169,0.0008042003,0.004762816,0.001942442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001529323,0.0003620088,0.5712671,0.002705743,0.0001513272,0.001981202,0.2114606,0.0002039582,0.001430578,0.004944998,0.02016394,0.1851756],"study_design_scores_gemma":[0.00001379631,0.0004676061,0.3524652,0.004229913,0.00008464019,0.007325229,0.5045439,0.001041723,0.0006456196,0.002437768,0.126633,0.0001115479],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9604198,0.01618596,0.002239635,0.01075753,0.0001904215,0.000134875,0.0005369636,0.00003956811,0.009495174],"genre_scores_gemma":[0.9613071,0.02672738,0.001585643,0.007593762,0.0001682958,0.0001679377,0.0004571622,0.00006621884,0.00192643],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01014452,"threshold_uncertainty_score":0.05364996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1474093345032763,"score_gpt":0.442678039612974,"score_spread":0.2952687051096977,"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."}}