{"id":"W3046050249","doi":"10.5465/ambpp.2020.22076symposium","title":"Artificial Intelligence and Innovation Ethics","year":2020,"lang":"en","type":"article","venue":"Academy of Management Proceedings","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Scale (ratio); Test (biology); Intersection (aeronautics); Business ethics; Computer science; Business intelligence; Human intelligence; Artificial intelligence; Knowledge management; Data science; Engineering ethics; Political science; Public relations; Engineering","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":[0.02186661,0.0006814974,0.001028692,0.001555337,0.008012366,0.01377458,0.001529003,0.01295034,0.006600988],"category_scores_gemma":[0.01900597,0.0003965559,0.0009214837,0.001237909,0.0407679,0.009812749,0.007304555,0.01241451,0.001845245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009471052,"about_ca_system_score_gemma":0.009657753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002138126,"about_ca_topic_score_gemma":0.001837857,"domain_scores_codex":[0.9760292,0.01582976,0.000793746,0.001540935,0.004058313,0.001747957],"domain_scores_gemma":[0.9854413,0.01004373,0.0007261452,0.001286389,0.001594466,0.0009080681],"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":[0.000002143704,0.000004645248,0.00003258455,0.00001972148,0.000001935063,0.00001126559,0.0007493381,0.00007849286,0.00001740548,0.9903361,0.005817486,0.002929041],"study_design_scores_gemma":[0.000005566628,0.00001068704,0.00008459661,0.0001754332,0.000002381357,0.00004476519,0.0005664204,0.0002594603,0.00005425993,0.8236825,0.175105,0.000008914792],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.004719139,0.0289405,0.02435534,0.4566179,0.00377117,0.0000956459,0.00005520035,0.00007125025,0.4813738],"genre_scores_gemma":[0.6474868,0.03792803,0.02532347,0.1409554,0.009018848,0.0008048267,0.0001149794,0.0002214612,0.1381461],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02186661,"threshold_uncertainty_score":0.115643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2690322674638286,"score_gpt":0.4246476369407941,"score_spread":0.1556153694769655,"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."}}