{"id":"W7134309406","doi":"","title":"A friendly face: do text-to-image systems rely on stereotypes when the input is under-specified?","year":2023,"lang":"en","type":"article","venue":"NPARC","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Salient; Popularity; Diversity (politics); Portrait; Stereotype (UML); Cognition; Cognitive bias","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.00325119,0.0002952604,0.0003172247,0.0004720211,0.0004627392,0.002256736,0.0006818888,0.0008267486,0.007692665],"category_scores_gemma":[0.04980857,0.0002551701,0.0003215368,0.0002167543,0.0008147353,0.004947061,0.001484529,0.0009064223,0.002688709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005180251,"about_ca_system_score_gemma":0.0003084466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00137106,"about_ca_topic_score_gemma":0.0007165062,"domain_scores_codex":[0.9978517,0.0006641647,0.00009127488,0.0006311899,0.0005517561,0.000209971],"domain_scores_gemma":[0.9766442,0.01259313,0.003561396,0.003856207,0.002566202,0.0007787354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003253848,0.0006379032,0.2433479,0.001209024,0.0002059675,0.001064668,0.02186811,0.003319211,0.3345674,0.0202185,0.01212705,0.3581805],"study_design_scores_gemma":[0.0001925046,0.001546726,0.7085369,0.0005343251,0.0002739,0.003194747,0.01579268,0.06631931,0.1158901,0.05907084,0.0282819,0.0003660324],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9243544,0.0002653424,0.03008576,0.0006955023,0.0001209625,0.0002047488,0.0004691318,0.0005827775,0.04322122],"genre_scores_gemma":[0.9890949,0.00008865275,0.007406883,0.0004529656,0.00004523666,0.00008052948,0.0003843982,0.000175839,0.002270465],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007692665,"threshold_uncertainty_score":0.02573454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07625469674617368,"score_gpt":0.3032012094765997,"score_spread":0.226946512730426,"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."}}