{"id":"W4412933965","doi":"10.1109/ethics65148.2025.11098388","title":"Analyzing Technosolutionism in Synthetic Face Data Companies","year":2025,"lang":"en","type":"article","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Face (sociological concept); Computer science","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.01579461,0.0003559066,0.000261348,0.001315152,0.001030416,0.002156499,0.0009787155,0.001392566,0.001360762],"category_scores_gemma":[0.06720948,0.0001898521,0.0004781021,0.00130687,0.002365524,0.001555397,0.001924606,0.001451637,0.0003872045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0017048,"about_ca_system_score_gemma":0.0006724579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002499042,"about_ca_topic_score_gemma":0.00218511,"domain_scores_codex":[0.9886769,0.006708018,0.0004163558,0.0009343716,0.002827136,0.0004372396],"domain_scores_gemma":[0.9330024,0.04935226,0.004550634,0.007562791,0.005016237,0.000515574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.001609006,0.001497728,0.3544166,0.0006015393,0.0003532492,0.001159332,0.01325361,0.1663312,0.009057164,0.1534642,0.02456343,0.2736929],"study_design_scores_gemma":[0.00008909461,0.0006457963,0.09455994,0.0002807654,0.00006045398,0.001035296,0.007996777,0.7705227,0.01618045,0.0868474,0.02166605,0.0001152908],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9084558,0.0004837757,0.07541209,0.00257791,0.0001297778,0.0002870845,0.0009655398,0.0001615197,0.01152648],"genre_scores_gemma":[0.9807093,0.000087067,0.01671349,0.0003558384,0.00004312082,0.0001529866,0.000982476,0.00003182433,0.0009239434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9989696,"threshold_uncertainty_score":0.08353084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03220377751626325,"score_gpt":0.2891872890866203,"score_spread":0.256983511570357,"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."}}