{"id":"W2892038960","doi":"10.1145/3287560.3287564","title":"Fairness through Causal Awareness","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":105,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Replicate; Computer science; Outcome (game theory); Causal model; Observational study; Machine learning; Artificial intelligence; Generative grammar; Confounding; Intervention (counseling); Causal structure; Affect (linguistics); Econometrics; Cognitive psychology; Psychology; Statistics; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001231663,0.0001495634,0.0002322405,0.0000289004,0.0000338712,0.000021606,0.0002119792,0.00009397951,0.001636677],"category_scores_gemma":[0.0001448811,0.0001194239,0.00004695227,0.0001287442,0.00003664757,0.0003466714,0.00009677828,0.0001229247,0.0004729304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004637926,"about_ca_system_score_gemma":0.00003947998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006333138,"about_ca_topic_score_gemma":0.00002984279,"domain_scores_codex":[0.999104,0.00002652236,0.0002041226,0.0002307701,0.0001907408,0.000243809],"domain_scores_gemma":[0.9990395,0.0002586337,0.00006585012,0.0004956711,0.0001048242,0.00003549881],"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":[0.000005557004,0.00004481535,0.003224573,0.00005374103,0.00000997425,0.00000340664,0.0001835048,0.000002902262,0.00202069,0.9902676,0.003681001,0.0005021802],"study_design_scores_gemma":[0.0001601054,0.00005622449,0.0001669593,0.00003376307,0.000007699331,0.000006026986,0.0002286241,0.00008278948,0.057501,0.9368688,0.004662724,0.0002252501],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2923483,0.00001394765,0.5691824,0.0001347386,0.000294495,0.0004230062,0.000004134936,0.001373652,0.1362253],"genre_scores_gemma":[0.8815256,0.000007253183,0.1103088,0.0001937753,0.00005596973,0.0000339073,0.000003754861,0.00003412324,0.007836896],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5891773,"threshold_uncertainty_score":0.999276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.179181385939629,"score_gpt":0.4409086553286289,"score_spread":0.261727269389,"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."}}