{"id":"W4409157115","doi":"10.1126/sciadv.ads4593","title":"FairDiffusion: Enhancing equity in latent diffusion models via fair Bayesian perturbation","year":2025,"lang":"en","type":"article","venue":"Science Advances","topic":"AI in cancer detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Eye Institute","keywords":"Generative grammar; Computer science; Equity (law); Generative model; Health equity; Bayesian probability; Machine learning; Modalities; Artificial intelligence; Health care; Data science; Political science; Sociology","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":[0.006153943,0.00115614,0.00139233,0.001089246,0.0009458606,0.001987247,0.002491471,0.002165525,0.004430306],"category_scores_gemma":[0.02425024,0.0006477297,0.001155824,0.0007402721,0.002107407,0.002772053,0.003867348,0.002912725,0.0008097455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001985394,"about_ca_system_score_gemma":0.001829533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005323437,"about_ca_topic_score_gemma":0.005517246,"domain_scores_codex":[0.9979158,0.001045376,0.0000697314,0.0004356023,0.0003840033,0.0001494708],"domain_scores_gemma":[0.9864354,0.01092624,0.0004825215,0.001073438,0.000700159,0.0003823515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003526047,0.0002055525,0.002882273,0.0001969067,0.00009634087,0.0001910103,0.000374665,0.7948992,0.004335857,0.07652357,0.005518033,0.114424],"study_design_scores_gemma":[0.00002942423,0.00003009277,0.0001080908,0.00001513391,0.000008050636,0.00002739401,0.00001435808,0.9653181,0.0009672094,0.03271285,0.0007589183,0.00001025541],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02486298,0.0005649207,0.969425,0.0008015906,0.00008453111,0.0001315574,0.0002106104,0.001033953,0.002884883],"genre_scores_gemma":[0.7338058,0.0005279474,0.2548605,0.001328951,0.0001895348,0.0003914591,0.0007824419,0.0006839941,0.007429365],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006153943,"threshold_uncertainty_score":0.03254557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0149522578588757,"score_gpt":0.3042683386813487,"score_spread":0.289316080822473,"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."}}