{"id":"W4312322420","doi":"10.2139/ssrn.4269591","title":"Leveraging 'Responsible, Explainable, and Local Artificial Intelligence Solutions for Clinical Public Health in the Global South' (REL-AI4GS): Implications for Policies and Lessons Learned from the 'Africa-Canada Artificial Intelligence and Data Innovation Consortium' (ACADIC) project","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada); York University; University of Toronto","funders":"","keywords":"Public health; Political science; Public relations; Public administration; Medicine; Nursing","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":[0.08748727,0.0006810637,0.0005740916,0.001802892,0.004437176,0.014253,0.005349255,0.01373164,0.009499769],"category_scores_gemma":[0.1291306,0.0008768548,0.001287489,0.002020657,0.01703157,0.01140603,0.0221133,0.0137423,0.0008308601],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02558127,"about_ca_system_score_gemma":0.215176,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1173419,"about_ca_topic_score_gemma":0.1244321,"domain_scores_codex":[0.9611704,0.02407251,0.001316813,0.002682443,0.005385864,0.005371993],"domain_scores_gemma":[0.8181413,0.1118108,0.01396525,0.01597302,0.01430527,0.02580437],"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":[0.00008142848,0.000399944,0.01158473,0.0006880295,0.000223817,0.0004113159,0.008296608,0.01420663,0.001053497,0.8256021,0.07366069,0.0637913],"study_design_scores_gemma":[0.0004129298,0.0002564275,0.01646936,0.001818361,0.000127854,0.00018234,0.009754565,0.01358962,0.002001813,0.6803634,0.2747742,0.0002491748],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.01721655,0.001096853,0.03200164,0.9104092,0.0007343963,0.0002984729,0.0002340722,0.0003810721,0.03762775],"genre_scores_gemma":[0.7777597,0.002327637,0.06695921,0.1385393,0.000934664,0.0006006843,0.0005428288,0.0002728999,0.012063],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9744187,"threshold_uncertainty_score":0.4626824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6015438832217961,"score_gpt":0.5031017025012124,"score_spread":0.09844218072058364,"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."}}