{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007928004,0.00002523945,0.00004009961,0.00002997824,0.0003241277,0.0000442673,0.00007705131,0.00008968526,0.0009414069],"category_scores_gemma":[0.0005108062,0.00002359525,0.00001593336,0.0001756058,0.0003636874,0.0002351024,0.00001330386,0.00008223195,0.0001467835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000511076,"about_ca_system_score_gemma":0.0001685384,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005739961,"about_ca_topic_score_gemma":0.05598376,"domain_scores_codex":[0.99945,0.00006528434,0.00006727409,0.000065008,0.0002070127,0.0001453892],"domain_scores_gemma":[0.999653,0.000116604,0.00001493497,0.00004128243,0.000119631,0.00005455914],"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":[0.000001434272,0.00001062306,0.008589459,2.523052e-7,0.000001021524,5.812363e-7,0.01467793,2.319222e-7,0.000008803682,0.9749365,0.001318035,0.000455118],"study_design_scores_gemma":[0.0002279256,0.00003230515,0.1077265,0.000008878705,0.000001508113,9.474645e-8,0.008737572,0.00002346309,0.00003399172,0.685615,0.1974735,0.0001193366],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1887215,0.000004407837,0.0002692951,0.01640279,0.0001670641,0.00004261056,4.522832e-7,0.00002548456,0.7943664],"genre_scores_gemma":[0.9905418,0.00001184998,0.000554704,0.0009359299,0.0004040271,0.000001080722,5.193676e-7,0.000002213414,0.00754789],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8018203,"threshold_uncertainty_score":0.9999719,"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."}}