{"id":"W4390952029","doi":"10.23977/jaip.2023.060812","title":"The Application of Artificial Intelligence in Enterprise Auditing","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Research studies in Vietnam","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Audit; Computer science; Mindset; Audit plan; Information technology audit; Performance audit; Joint audit; Scope (computer science); Business intelligence; Internal audit; Work (physics); Information security audit; Process management; Knowledge management; Business; Accounting; Artificial intelligence; Engineering; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007004306,0.0001590871,0.0002704505,0.0001842833,0.0003242726,0.0001014603,0.0009417451,0.000073893,0.0001461695],"category_scores_gemma":[0.01232428,0.0001198697,0.0001349594,0.001794138,0.0006441723,0.000870127,0.0004568924,0.0006875002,0.0007351399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002676687,"about_ca_system_score_gemma":0.00006457556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002014028,"about_ca_topic_score_gemma":0.0003050457,"domain_scores_codex":[0.9961254,0.0003983111,0.00157197,0.000262364,0.001156828,0.0004851034],"domain_scores_gemma":[0.9944837,0.003575791,0.001244225,0.0003712833,0.000210793,0.0001142275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004680589,0.0002820724,0.001120833,0.00001356909,0.00003653468,0.00006289904,0.002804748,0.02776128,0.01185423,0.005628283,0.0005715337,0.949396],"study_design_scores_gemma":[0.0000945721,0.001567198,0.00579082,0.0003180922,0.0001459816,0.0002423411,0.0977881,0.4395539,0.1476863,0.2446193,0.06130239,0.0008910774],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2735617,0.0006053972,0.6855774,0.02264775,0.002403949,0.001468359,0.00001044366,0.00007844453,0.01364657],"genre_scores_gemma":[0.9934263,0.001530308,0.004620166,0.00008774303,0.0002435093,0.0000225469,6.820126e-7,0.00001762646,0.00005114056],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9485049,"threshold_uncertainty_score":0.9959953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05115084884072752,"score_gpt":0.3894133173915131,"score_spread":0.3382624685507856,"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."}}