{"id":"W4391533129","doi":"10.2139/ssrn.4716668","title":"How Learning About Harms Impacts the Optimal Rate of Artificial Intelligence Adoption","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Computer science; Business; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004629238,0.0001657853,0.000265836,0.0002902903,0.0001961577,0.0004110401,0.0003282275,0.0000975559,0.00007119555],"category_scores_gemma":[0.0007032036,0.0001361214,0.0002004766,0.0004353421,0.00008325885,0.000495904,0.00004610512,0.002236796,0.0001414813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008665381,"about_ca_system_score_gemma":0.0008791735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001069417,"about_ca_topic_score_gemma":0.00008298382,"domain_scores_codex":[0.997649,0.0000604042,0.0005481885,0.0002546026,0.00006668673,0.001421159],"domain_scores_gemma":[0.9991517,0.0001827649,0.0003665926,0.000172128,0.00004653544,0.00008028632],"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":[0.00006225145,0.0000292723,0.001365051,0.00003802964,0.0002178729,0.000005831186,0.001127028,0.006671224,0.00075463,0.9339578,0.00006961736,0.05570144],"study_design_scores_gemma":[0.0002069659,0.0006904429,0.002034901,0.000153199,0.00005056833,0.0003319716,0.003201154,0.06244931,0.001278587,0.9139219,0.01522618,0.0004548536],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5240459,0.02953656,0.4356289,0.008815936,0.00103467,0.0002163736,0.0000152769,0.00007112649,0.0006352396],"genre_scores_gemma":[0.9888148,0.008991585,0.00006886796,0.0001065092,0.0004472299,0.000003512511,0.000002858359,0.00003127276,0.001533319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4647689,"threshold_uncertainty_score":0.9717891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03492834311497787,"score_gpt":0.2687864685198841,"score_spread":0.2338581254049063,"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."}}