{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05310955,0.001219002,0.002527679,0.002023198,0.002082529,0.006261648,0.002584857,0.00370042,0.007948914],"category_scores_gemma":[0.264017,0.001247273,0.001564769,0.001554571,0.00816852,0.01444676,0.004936179,0.007632308,0.0006934059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00335168,"about_ca_system_score_gemma":0.004216831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003117632,"about_ca_topic_score_gemma":0.002551404,"domain_scores_codex":[0.9689251,0.021914,0.001146846,0.004654775,0.002401404,0.000957843],"domain_scores_gemma":[0.7557998,0.2027858,0.01230139,0.02291885,0.00417512,0.002018976],"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":[0.000389137,0.0001770407,0.01330256,0.0004942251,0.0004627357,0.0002208975,0.001363245,0.06471307,0.000449499,0.8094408,0.006105473,0.1028813],"study_design_scores_gemma":[0.00004121727,0.00002865222,0.0007061316,0.00009165939,0.00004556188,0.00004597097,0.00006341194,0.04230369,0.0002138413,0.9544368,0.002001742,0.00002131814],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04542958,0.002359374,0.9109584,0.02715298,0.0004209935,0.0002696399,0.001060704,0.0005274704,0.01182086],"genre_scores_gemma":[0.8613058,0.001513871,0.1266904,0.005193129,0.0007891429,0.000541598,0.0006265395,0.0001969094,0.003142543],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05310955,"threshold_uncertainty_score":0.2808735,"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."}}