{"id":"W4289516857","doi":"10.1186/s40878-022-00305-0","title":"An eye for an ‘I:’ a critical assessment of artificial intelligence tools in migration and asylum management","year":2022,"lang":"en","type":"article","venue":"Comparative Migration Studies","topic":"Migration, Health and Trauma","field":"Psychology","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Refugee; Government (linguistics); Immigration; Big data; Asylum seeker; Computer science; Identification (biology); Artificial intelligence; National security; Biometrics; Computer security; Political science; Law","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000709669,0.0001522655,0.0003447913,0.0002168333,0.0003754352,0.000036374,0.0001057939,0.00003110707,0.00008007829],"category_scores_gemma":[0.00003458931,0.0001532961,0.00003670931,0.000277562,0.0001422994,0.0002639596,0.00003990611,0.0001265506,0.000002330305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008610581,"about_ca_system_score_gemma":0.00003669711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001412109,"about_ca_topic_score_gemma":0.01114755,"domain_scores_codex":[0.9980559,0.0004888434,0.0006115,0.0004082409,0.0002156915,0.0002197837],"domain_scores_gemma":[0.9990177,0.0003461138,0.000143548,0.0002133842,0.0002147187,0.00006453851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0006899994,0.002302402,0.04193308,0.0002247933,0.000206753,0.000004776763,0.1962412,0.001710113,0.0009067788,0.7317077,0.002476763,0.02159569],"study_design_scores_gemma":[0.0006990587,0.004392884,0.4972402,0.00003548534,0.0000929076,0.000003526429,0.4306468,0.04097436,0.0005587517,0.02155176,0.003391361,0.0004128596],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9504186,0.0009282732,0.04574412,0.0008644438,0.0003399477,0.001188186,0.00008885617,0.00002778369,0.0003997907],"genre_scores_gemma":[0.9930774,0.0000591915,0.004793475,0.0001447567,0.00006091669,0.001666525,0.0001251474,0.000007855973,0.00006472912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7101559,"threshold_uncertainty_score":0.6251233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3129151216021187,"score_gpt":0.5419333040569383,"score_spread":0.2290181824548196,"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."}}