{"id":"W4389332325","doi":"10.1109/tifs.2023.3340094","title":"Classification Utility, Fairness, and Compactness via Tunable Information Bottleneck and Rényi Measures","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Information bottleneck method; Compact space; Computer science; Categorical variable; Bottleneck; Representation (politics); Key (lock); Artificial intelligence; Mutual information; Machine learning; Data mining; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0005149,0.0001729415,0.0001721152,0.0003655407,0.0004513612,0.0004934415,0.001061098,0.0001647728,0.00000352669],"category_scores_gemma":[0.0002213666,0.0001684443,0.00002846927,0.0005872356,0.0001952113,0.007597148,0.000301003,0.0002665203,0.00002808528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003788378,"about_ca_system_score_gemma":0.00003711573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009192633,"about_ca_topic_score_gemma":0.0000332065,"domain_scores_codex":[0.9988028,0.00003589702,0.0003964755,0.000187307,0.0003401295,0.0002374276],"domain_scores_gemma":[0.9982692,0.0001016448,0.0001535701,0.001196912,0.0001830409,0.00009566035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006988519,0.00006040684,0.000768272,0.000463012,0.00007381648,0.000001289127,0.005349075,0.0003354719,0.00009890844,0.0361091,0.02038918,0.9362816],"study_design_scores_gemma":[0.0004780162,0.00006206421,0.007236111,0.0000308192,0.00001255006,0.0000227188,0.0004357188,0.8523009,0.001300197,0.1321017,0.005784045,0.0002351664],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06782032,0.00002210567,0.9260236,0.004326873,0.0003218689,0.0002864747,0.0001093192,0.0006605927,0.0004288224],"genre_scores_gemma":[0.9940263,0.0005625363,0.005138344,0.0001683103,0.00000627965,0.00002973875,0.00006128681,0.000004245013,0.00000296639],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9360464,"threshold_uncertainty_score":0.6868957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03020638483047152,"score_gpt":0.2509860788746621,"score_spread":0.2207796940441905,"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."}}