{"id":"W2908766589","doi":"10.1145/3278721.3278733","title":"Fairness in Relational Domains","year":2018,"lang":"en","type":"article","venue":"","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Probabilistic logic; Inference; Statistical relational learning; Artificial intelligence; Machine learning; Domain (mathematical analysis); Fairness measure; A priori and a posteriori; Relational database; Data mining","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":[0.02382817,0.0008273933,0.001261918,0.002345445,0.003603694,0.007430229,0.002340231,0.002073301,0.005430326],"category_scores_gemma":[0.06475979,0.0007549832,0.001862483,0.002137443,0.008183497,0.01400001,0.006960499,0.003709765,0.0007100638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0035395,"about_ca_system_score_gemma":0.003458911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003731976,"about_ca_topic_score_gemma":0.00213276,"domain_scores_codex":[0.9703609,0.01512137,0.002274065,0.005200234,0.005550406,0.001493055],"domain_scores_gemma":[0.9280306,0.05166827,0.004343368,0.009416749,0.004862404,0.001678607],"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":[0.00005774164,0.00003059604,0.001018972,0.00006962195,0.00003452293,0.0001015644,0.0003679205,0.01338831,0.0002723913,0.966432,0.000914683,0.01731154],"study_design_scores_gemma":[0.00001101791,0.00001055076,0.000135076,0.00002073488,0.0000115201,0.00005127749,0.0000603681,0.02741797,0.0002981971,0.96886,0.003113586,0.000009718789],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03095224,0.0006410321,0.9502286,0.00366133,0.0001265027,0.0001499856,0.0004091977,0.0002578568,0.01357327],"genre_scores_gemma":[0.7051982,0.0008431617,0.2844531,0.001355006,0.0005730506,0.0004633713,0.0007453972,0.0001566687,0.006211984],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02382817,"threshold_uncertainty_score":0.1260169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07277394491867763,"score_gpt":0.4091027960904194,"score_spread":0.3363288511717418,"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."}}