{"id":"W4386084222","doi":"10.2196/51494","title":"Can AI Mitigate Bias in Writing Letters of Recommendation?","year":2023,"lang":"en","type":"editorial","venue":"JMIR Medical Education","topic":"Diversity and Career in Medicine","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; JMIR Publications","funders":"Society of General Internal Medicine","keywords":"Task (project management); Computer science; Unconscious mind; Generative grammar; Recommender system; Implicit bias; Artificial intelligence; Natural language processing; Data science; Cognitive psychology; Psychology; World Wide Web; Social psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003557527,0.0001138938,0.0002673352,0.0002752993,0.0001381576,0.00004496952,0.0005312394,0.0005983069,0.001024045],"category_scores_gemma":[0.01414937,0.0001241756,0.00006300387,0.0006102577,0.0003654576,0.00010299,0.00006986997,0.0008518792,0.0000367802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003530654,"about_ca_system_score_gemma":0.005522155,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01196214,"about_ca_topic_score_gemma":0.007187968,"domain_scores_codex":[0.9964533,0.0003532651,0.0004063439,0.0002552807,0.002251987,0.0002798457],"domain_scores_gemma":[0.9978646,0.001258987,0.0002210502,0.0001288519,0.0002621902,0.0002643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004803757,0.0000851656,0.0006716486,0.0001028894,0.000008814262,0.000002872692,0.00982698,1.388612e-7,9.926533e-7,0.0002190798,0.9647018,0.02437486],"study_design_scores_gemma":[0.0002668279,0.00001816232,0.0004301126,0.000936545,0.0000181669,6.807259e-8,0.02445384,0.00000351338,0.000001457473,0.0003005434,0.9734493,0.0001213855],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.006804177,0.00002789031,0.000003033426,0.1171903,0.8721351,0.0002603013,0.00003909783,0.00005365932,0.003486451],"genre_scores_gemma":[0.01240296,0.0006343327,0.00003694215,0.004255119,0.9783013,0.0001194152,0.002183133,0.00002783085,0.002038992],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.1129351,"threshold_uncertainty_score":0.9998891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02735129233357322,"score_gpt":0.3875680935403085,"score_spread":0.3602168012067353,"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."}}