{"id":"W4402130453","doi":"10.1080/0960085x.2024.2395531","title":"Reducing the incidence of biased algorithmic decisions through feature importance transparency: an empirical study","year":2024,"lang":"en","type":"article","venue":"European Journal of Information Systems","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor; McMaster University; University of Waterloo","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Transparency (behavior); Computer science; Strategic information system; Empirical research; Feature (linguistics); Data science; Information system; Management information systems; Computer security; Statistics; Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.02966241,0.0004981285,0.0006094446,0.001384282,0.001087986,0.002265558,0.001776493,0.001852935,0.003341573],"category_scores_gemma":[0.2292082,0.0004181756,0.0006719134,0.001373402,0.002425449,0.004147728,0.001726568,0.00385017,0.0005941641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001226811,"about_ca_system_score_gemma":0.001401164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002067133,"about_ca_topic_score_gemma":0.001483665,"domain_scores_codex":[0.9734948,0.01915473,0.001550791,0.001563883,0.003378325,0.0008574894],"domain_scores_gemma":[0.4269578,0.5073988,0.03339311,0.02190947,0.008435966,0.001904733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00957107,0.04047702,0.5530301,0.002532922,0.0005655824,0.0009984274,0.02440108,0.02261362,0.009078331,0.01360503,0.004295645,0.3188312],"study_design_scores_gemma":[0.003480958,0.02390146,0.6627319,0.0009330569,0.001049143,0.001445986,0.01680966,0.2304231,0.02673579,0.01914487,0.01289938,0.0004447451],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954242,0.00007327466,0.002417879,0.0001678538,0.000007327917,0.0002585847,0.0000521489,0.00002452176,0.001574153],"genre_scores_gemma":[0.9948789,0.00005577656,0.004448537,0.00008936357,0.00001334546,0.0001730763,0.00005758641,0.00001008194,0.0002732669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02966241,"threshold_uncertainty_score":0.1568717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06517048671140001,"score_gpt":0.3321928754086463,"score_spread":0.2670223886972463,"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."}}