{"id":"W4412961042","doi":"10.1007/s43681-025-00787-5","title":"Faces and places: navigating the cross-border legal challenges of AI facial recognition technologies","year":2025,"lang":"en","type":"article","venue":"AI and Ethics","topic":"Law, AI, and Intellectual Property","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Patient Safety Institute","funders":"","keywords":"Facial recognition system; Computer science; Psychology; Political science; Artificial intelligence; Pattern recognition (psychology)","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":[0.008235097,0.0002431872,0.0003211817,0.001044152,0.005983934,0.01406038,0.00178728,0.004842242,0.01055314],"category_scores_gemma":[0.02197086,0.0003182103,0.0002914759,0.000669341,0.01519784,0.01778556,0.007783176,0.004649627,0.0014321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002753442,"about_ca_system_score_gemma":0.00438782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01547361,"about_ca_topic_score_gemma":0.0152655,"domain_scores_codex":[0.9962213,0.002042186,0.0001130866,0.0004004825,0.0007788896,0.000443963],"domain_scores_gemma":[0.9925487,0.004658042,0.0005502683,0.000794765,0.0009853229,0.000462813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006940476,0.00004429869,0.002218758,0.0000547906,0.000008531068,0.0003233162,0.01319248,0.002252558,0.0009073293,0.8539197,0.01383444,0.1131745],"study_design_scores_gemma":[0.00001139763,0.00003023497,0.001651554,0.0002559848,0.0000118537,0.0003558813,0.02958795,0.01047488,0.001377728,0.8315075,0.1246974,0.00003757832],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1292106,0.003686701,0.1749589,0.2238149,0.0007959549,0.0001110996,0.0001909205,0.0003180038,0.4669129],"genre_scores_gemma":[0.9551967,0.001545214,0.02325956,0.005056419,0.0002007913,0.00006330719,0.00007504755,0.0001129867,0.01449008],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01547361,"threshold_uncertainty_score":0.04355186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05252324434598227,"score_gpt":0.3813481173344154,"score_spread":0.3288248729884331,"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."}}