{"id":"W4388873627","doi":"10.1115/detc2023-114687","title":"Fairness- and Uncertainty-Aware Data Generation for Data-Driven Design","year":2023,"lang":"en","type":"article","venue":"","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Property (philosophy); Sampling (signal processing); Data mining; Generative Design; Selection (genetic algorithm); Generative model; Grid; Bayesian probability; Generative grammar; Machine learning; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000141172,0.00006660137,0.00006418576,0.00003881372,0.00005738321,0.00006590538,0.000222583,0.00003494576,0.00002735441],"category_scores_gemma":[0.00002366341,0.00005963933,0.000003899813,0.00006886919,0.000005817992,0.0003098271,0.00011837,0.0000245684,0.000008293735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006970226,"about_ca_system_score_gemma":0.000008442437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001331582,"about_ca_topic_score_gemma":0.0000377137,"domain_scores_codex":[0.9995438,0.000005360618,0.00008560365,0.0002098265,0.00005229008,0.0001030896],"domain_scores_gemma":[0.9994729,0.00003868483,0.000009613798,0.000436367,0.0000173278,0.00002511297],"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":[0.000001673252,0.00000142002,0.00001070114,0.00006972099,0.000009916741,3.055722e-7,0.00003149201,0.9567225,0.0001012839,0.00008795743,0.03261215,0.01035086],"study_design_scores_gemma":[0.000130039,0.000006625205,0.00003406817,0.000004610235,0.00001008497,4.091532e-7,0.00001885432,0.9932235,0.0007009855,0.00008732703,0.005699558,0.00008395026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001373872,0.0000386982,0.9975706,0.00009440017,0.0001245051,0.0001918574,0.0001275137,0.0003951236,0.00008342845],"genre_scores_gemma":[0.9039874,0.0005078503,0.0784037,0.0000613248,0.0003206264,0.00005930712,0.01584207,0.00005455973,0.000763137],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9191669,"threshold_uncertainty_score":0.2432021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1575439112816699,"score_gpt":0.2955893569451875,"score_spread":0.1380454456635176,"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."}}