{"id":"W4416214913","doi":"10.1109/mts.2025.3627873","title":"Why Do People Trust Physiognomic AI?","year":2025,"lang":"","type":"article","venue":"IEEE Technology and Society Magazine","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Antithesis; Field (mathematics); Cognition; Core (optical fiber); Big data; Overconfidence effect","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":[0.009578932,0.0002641832,0.000378139,0.001110496,0.003464201,0.006654291,0.0005595528,0.00290276,0.003468081],"category_scores_gemma":[0.04156581,0.0003288832,0.0002853041,0.0008900117,0.01513822,0.007686477,0.002594404,0.004094685,0.001183301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001854311,"about_ca_system_score_gemma":0.001300126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004733971,"about_ca_topic_score_gemma":0.003341525,"domain_scores_codex":[0.9911754,0.005158668,0.0002565177,0.0005837347,0.001989504,0.0008361223],"domain_scores_gemma":[0.9585729,0.01806755,0.008649121,0.003896978,0.006800982,0.004012418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002915069,0.0002272313,0.1690734,0.0005827787,0.0003962611,0.002430731,0.3140882,0.000693963,0.003487497,0.3093614,0.04549082,0.1538762],"study_design_scores_gemma":[0.00006634863,0.0003176344,0.06975339,0.0006995695,0.0001417984,0.002989009,0.2372473,0.00348685,0.001507756,0.4782483,0.2052989,0.0002430828],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5121529,0.00807157,0.03402714,0.3441452,0.001311296,0.00006650398,0.0001619707,0.0001826014,0.09988078],"genre_scores_gemma":[0.9850875,0.0009377805,0.0008974134,0.01096672,0.0001795693,0.00001411761,0.0000225483,0.00002401222,0.001870363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009578932,"threshold_uncertainty_score":0.05065882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01045936902493043,"score_gpt":0.3084806213777315,"score_spread":0.2980212523528011,"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."}}