{"id":"W4390080297","doi":"10.1093/geroni/igad104.2325","title":"STRATEGIES TO MITIGATE MACHINE LEARNING BIAS AFFECTING OLDER ADULTS: RESULTS FROM A SCOPING REVIEW","year":2023,"lang":"en","type":"review","venue":"Innovation in Aging","topic":"Technology Use by Older Adults","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University; Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"CINAHL; Artificial intelligence; Machine learning; Computer science; Digital library; Selection bias; Data science; Psychology; Psychological intervention; Statistics; Mathematics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05084592,0.001620595,0.004647526,0.02082652,0.00117369,0.005514379,0.002392548,0.003240983,0.004063444],"category_scores_gemma":[0.2155911,0.001132519,0.007521768,0.01541339,0.001652689,0.005048659,0.003734714,0.001968328,0.0005660335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006070112,"about_ca_system_score_gemma":0.02345591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0078024,"about_ca_topic_score_gemma":0.01678784,"domain_scores_codex":[0.968908,0.01164854,0.01271351,0.001229475,0.004810627,0.0006898236],"domain_scores_gemma":[0.7758881,0.1840997,0.01692477,0.002600347,0.01972635,0.0007606962],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001521799,0.00003701584,0.0008853695,0.8412656,0.003199,0.0001521407,0.001650482,0.0002384844,0.0002020248,0.001298778,0.002534217,0.1483846],"study_design_scores_gemma":[0.00003841373,0.00007599953,0.0009108019,0.9705335,0.008923144,0.0001112843,0.0006972942,0.00005796684,0.0001787305,0.0004808736,0.01797337,0.00001851645],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001737338,0.9930743,0.0009500361,0.001434268,0.000330735,0.001085523,0.0003173591,0.00001836739,0.001051922],"genre_scores_gemma":[0.01697227,0.9768697,0.002403706,0.001276268,0.0001410917,0.001883998,0.0002540029,0.00001416839,0.0001848283],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9491541,"threshold_uncertainty_score":0.2689021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.101282082370973,"score_gpt":0.4117821433995242,"score_spread":0.3105000610285512,"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."}}