{"id":"W4404518465","doi":"10.1609/aies.v7i1.31701","title":"Human-Centered AI Applications for Canada’s Immigration Settlement Sector","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI/ACM Conference on AI Ethics and Society","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; National Research Council Canada","funders":"","keywords":"Immigration; Settlement (finance); Political science; Geography; Business; Law; Finance","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002917509,0.0005478423,0.0002581021,0.001244357,0.003433893,0.004662497,0.00196419,0.00181323,0.01801237],"category_scores_gemma":[0.01011253,0.0002080136,0.0004742293,0.001303316,0.00180302,0.0015152,0.004498066,0.001615477,0.003825439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009125241,"about_ca_system_score_gemma":0.01713719,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3496597,"about_ca_topic_score_gemma":0.5367966,"domain_scores_codex":[0.9983316,0.0007120624,0.00007240891,0.000147279,0.000469848,0.0002668091],"domain_scores_gemma":[0.9947101,0.002143548,0.0001464017,0.0005934362,0.001406706,0.0009997671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004505343,0.0004854663,0.01195261,0.001023184,0.00007186981,0.001810533,0.02159452,0.02135045,0.008831711,0.1253643,0.2490061,0.5580587],"study_design_scores_gemma":[0.00007474345,0.00008540432,0.00659425,0.0005790353,0.00002836106,0.0003430574,0.00779024,0.05254653,0.002901456,0.06160393,0.8673305,0.0001226012],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05399644,0.002310717,0.5552452,0.03952337,0.00101049,0.002177831,0.003830681,0.03544297,0.3064623],"genre_scores_gemma":[0.4424393,0.003563971,0.4662,0.004578194,0.000239569,0.001988896,0.004509924,0.001706096,0.07477407],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6503403,"threshold_uncertainty_score":0.695249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1924242159839569,"score_gpt":0.4128143343969563,"score_spread":0.2203901184129993,"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."}}