{"id":"W3135367144","doi":"10.1162/neco_a_01468","title":"TARA: Training and Representation Alteration for AI Fairness and Domain Generalization","year":2022,"lang":"en","type":"preprint","venue":"Neural Computation","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Debiasing; Computer science; Representation (politics); Machine learning; Artificial intelligence; Baseline (sea); Generalization; Set (abstract data type); Domain (mathematical analysis); Training set; Independence (probability theory); Pareto principle; Mathematics; Statistics; Psychology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004628326,0.000241349,0.0002796183,0.0002216866,0.0004937939,0.0005729233,0.0002566171,0.0001260236,0.000003961716],"category_scores_gemma":[0.0001183344,0.0002806101,0.00005422569,0.0002137904,0.00003986897,0.0007051112,0.0007727341,0.0004217566,2.596484e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009196846,"about_ca_system_score_gemma":0.00006761001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005733153,"about_ca_topic_score_gemma":0.000008646943,"domain_scores_codex":[0.9978693,0.0003860481,0.0003873302,0.0008338736,0.0003233409,0.0002000922],"domain_scores_gemma":[0.998949,0.0002432993,0.0003785083,0.0002295703,0.0001394807,0.00006017342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001754928,0.000009641757,0.0004991425,0.00009492515,0.00001476567,0.000003401502,0.005017699,0.9117783,0.0002200206,0.01458651,0.00008444353,0.06767356],"study_design_scores_gemma":[0.0005382785,0.00008404112,0.002757642,0.0000210436,0.00001901044,0.00001686248,0.0001578006,0.9385259,0.00003433909,0.05744689,0.0001498556,0.0002484048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1509199,0.000103401,0.845065,0.001881904,0.001070722,0.0007384723,0.000007875491,0.0001822317,0.00003046359],"genre_scores_gemma":[0.845428,0.00001288677,0.1531545,0.0003687148,0.0002561806,0.0001615615,0.0005677555,0.00002620391,0.00002418345],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6945081,"threshold_uncertainty_score":0.9999646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04785894605328452,"score_gpt":0.3429036926849287,"score_spread":0.2950447466316442,"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."}}