{"id":"W4382203029","doi":"10.1609/aaai.v37i8.26196","title":"A Fair Generative Model Using LeCam Divergence","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; Korea Advanced Institute of Science and Technology; National Research Foundation of Korea; Institute for Information and Communications Technology Promotion; Korea Customs Service; National Research Foundation","keywords":"Benchmark (surveying); Divergence (linguistics); Measure (data warehouse); Computer science; Set (abstract data type); Range (aeronautics); Generative grammar; Sample size determination; Generative model; Statistical power; Sample (material); Machine learning; Data mining; Artificial intelligence; 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":[0.009847987,0.001142137,0.001836153,0.001190319,0.001254525,0.00326165,0.003810372,0.002617518,0.005204398],"category_scores_gemma":[0.02545132,0.0008081637,0.001292444,0.0009532074,0.003403406,0.003609403,0.003483979,0.003437491,0.0008507002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002983261,"about_ca_system_score_gemma":0.002190962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005130228,"about_ca_topic_score_gemma":0.004888413,"domain_scores_codex":[0.9971444,0.00147849,0.00008233068,0.0006014126,0.0004280971,0.0002652294],"domain_scores_gemma":[0.9886861,0.008469721,0.0006201065,0.0008902553,0.000848902,0.0004849276],"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":[0.00007681215,0.00005163257,0.001445605,0.00005463679,0.00004445418,0.0001184921,0.0002308182,0.7171284,0.0009760411,0.2620576,0.002526922,0.01528865],"study_design_scores_gemma":[0.00000943363,0.00001333657,0.0000815239,0.00001096506,0.000005453511,0.00001916453,0.00001138931,0.9316923,0.0002062729,0.06742388,0.000515181,0.00001118661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01583713,0.0002263171,0.9799033,0.0007776402,0.00006272576,0.00008265702,0.0001454928,0.0001966622,0.002767989],"genre_scores_gemma":[0.7503682,0.0004766526,0.2278853,0.001348422,0.0002352822,0.0007976622,0.0007912737,0.0004927907,0.01760457],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009847987,"threshold_uncertainty_score":0.05208176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2930600442660267,"score_gpt":0.4240172083587,"score_spread":0.1309571640926733,"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."}}