{"id":"W4416017596","doi":"10.1145/3746252.3761591","title":"Advances in Financial AI: Innovations, Risk, and Responsibility in the Era of LLMs","year":2025,"lang":"en","type":"article","venue":"","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Collège Boréal","funders":"","keywords":"Relevance (law); Event (particle physics); Financial sector; Financial services; Financial market; Core (optical fiber); Risk management","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":[],"consensus_categories":[],"category_scores_codex":[0.03135082,0.0004986837,0.0004912649,0.00179683,0.003411824,0.01593653,0.001772466,0.006673754,0.006368399],"category_scores_gemma":[0.05791286,0.0003245228,0.0006184386,0.001958949,0.01744783,0.02036715,0.00590738,0.01306134,0.001320269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007767939,"about_ca_system_score_gemma":0.009387432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001562172,"about_ca_topic_score_gemma":0.001631856,"domain_scores_codex":[0.9845823,0.009266916,0.0007050769,0.0008048066,0.003729824,0.0009110805],"domain_scores_gemma":[0.9470649,0.03908347,0.001529774,0.003443495,0.005525175,0.00335322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002444248,0.00002378813,0.0003462125,0.0002214062,0.000009308027,0.00009409232,0.003783501,0.001020079,0.0001673767,0.8891085,0.04955176,0.05564957],"study_design_scores_gemma":[0.000006870918,0.00001938509,0.0002778653,0.0005410665,0.000004996974,0.00009007683,0.001978307,0.001545226,0.0002470744,0.6257861,0.3694783,0.00002489959],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.00865445,0.05106452,0.02644812,0.8308526,0.01097793,0.00003688004,0.00009503847,0.0001457375,0.07172477],"genre_scores_gemma":[0.7043551,0.09326966,0.03991199,0.08517819,0.02285951,0.0001989135,0.0002078103,0.00035362,0.05366527],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03135082,"threshold_uncertainty_score":0.165801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01272827444553454,"score_gpt":0.3072507192213175,"score_spread":0.2945224447757829,"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."}}