{"id":"W3132791097","doi":"10.5267/j.ac.2021.2.009","title":"An analysis of auditors’ perceptions towards artificial intelligence and its contribution to audit quality","year":2021,"lang":"en","type":"article","venue":"Accounting","topic":"Auditing, Earnings Management, Governance","field":"Business, Management and Accounting","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Audit; Quality audit; Perception; Usability; Information technology audit; Accounting; Internal audit; Knowledge management; Business; Quality (philosophy); Computer science; Process management; Joint audit; Psychology; Human–computer interaction","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006971407,0.0001378097,0.0002183305,0.001394025,0.0008756266,0.002222816,0.000238016,0.0003376495,0.00153934],"category_scores_gemma":[0.0260171,0.0002018461,0.0003050039,0.001456166,0.001073953,0.0008075559,0.001031095,0.0006520101,0.0001526162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001361562,"about_ca_system_score_gemma":0.001418671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008750855,"about_ca_topic_score_gemma":0.009585294,"domain_scores_codex":[0.9956507,0.002069976,0.0004876209,0.0001354763,0.001283075,0.0003731911],"domain_scores_gemma":[0.9518546,0.01825372,0.01638997,0.001453987,0.009307257,0.002740457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001596909,0.0001790759,0.9298828,0.00009945843,0.00003684151,0.0001275715,0.04782926,0.0001795023,0.001082334,0.0003160895,0.0003563574,0.01975103],"study_design_scores_gemma":[0.00001197077,0.000348877,0.8997236,0.00008689021,0.00002529347,0.0001333843,0.09596643,0.000725643,0.0004183275,0.0001775697,0.002350722,0.00003120667],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989985,0.00004790481,0.00008746804,0.00007559772,0.000001482752,0.000009957951,0.00001098386,0.000001513994,0.0007666714],"genre_scores_gemma":[0.9995061,0.00008109196,0.0001055056,0.0000287518,0.000003293769,0.000006059151,0.00001508281,8.17024e-7,0.0002531376],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008750855,"threshold_uncertainty_score":0.03686875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02594924307493069,"score_gpt":0.2959430587018785,"score_spread":0.2699938156269478,"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."}}