{"id":"W4320481778","doi":"10.1007/978-3-031-25069-9_13","title":"FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learning","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Skin lesion; Computer science; Resampling; Artificial intelligence; Deep learning; Lesion; Feature (linguistics); Pattern recognition (psychology); Machine learning; Task (project management); Dermatology; Medicine; Pathology","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":[0.0006850323,0.0005648658,0.0004085803,0.0006633511,0.0003705144,0.001468325,0.001242419,0.0009979579,0.02267549],"category_scores_gemma":[0.002354579,0.0002473439,0.0003703825,0.0004553684,0.0008373744,0.001956316,0.001664782,0.001891144,0.003566193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005376365,"about_ca_system_score_gemma":0.0004722758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001583604,"about_ca_topic_score_gemma":0.002832329,"domain_scores_codex":[0.9997502,0.00003553301,0.000008201549,0.00005687404,0.0001271616,0.0000219706],"domain_scores_gemma":[0.9993466,0.0003949978,0.00001929433,0.0000886247,0.00009398498,0.00005657216],"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":[0.0001482575,0.00009851093,0.0002665697,0.0002176058,0.00003174831,0.0001149731,0.0001507004,0.01811778,0.01241448,0.093458,0.05640758,0.8185738],"study_design_scores_gemma":[0.00004738409,0.0001333435,0.0007125126,0.0001355969,0.00003124644,0.0004843868,0.00009426534,0.4019979,0.02258461,0.364237,0.2094699,0.00007201525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004793637,0.002931104,0.9329552,0.001421673,0.001116209,0.00009044907,0.0002480481,0.00417091,0.05227286],"genre_scores_gemma":[0.165863,0.002732445,0.7125043,0.001336924,0.0009193879,0.0001570188,0.0006129814,0.00123899,0.1146349],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02267549,"threshold_uncertainty_score":0.07585704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01213528635741349,"score_gpt":0.258569938422091,"score_spread":0.2464346520646775,"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."}}