{"id":"W4283794045","doi":"10.1609/aaai.v36i11.21581","title":"Socially Intelligent Affective AI","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Psychiatry, Mental Health, Neuroscience","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Affect (linguistics); Affective computing; Feeling; Context (archaeology); Computer science; Plan (archaeology); Focus (optics); Event (particle physics); Emotion detection; Cognitive psychology; Task (project management); Artificial intelligence; Social intelligence; Human–computer interaction; Psychology; Social psychology; Emotion recognition; Communication; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001042649,0.0002947367,0.0003055923,0.000217698,0.001089072,0.0002581925,0.004832404,0.00006010419,0.0001662685],"category_scores_gemma":[0.000236117,0.0002528051,0.0001931868,0.001697765,0.0004123872,0.0005168468,0.001703974,0.0008226829,0.00009075301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002959886,"about_ca_system_score_gemma":0.0006183231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000772509,"about_ca_topic_score_gemma":0.0000249951,"domain_scores_codex":[0.9964517,0.00008703065,0.0006478642,0.0008966636,0.001291517,0.0006252755],"domain_scores_gemma":[0.9983084,0.000130235,0.0005317784,0.000479473,0.0003827228,0.0001674383],"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":[0.0000627211,0.0003909574,0.0001959338,0.00002851419,0.000007333063,0.000001066336,0.002619214,0.0001276743,0.01933835,0.9302565,0.0007526197,0.04621912],"study_design_scores_gemma":[0.00005414206,0.001266415,0.000441465,0.00009348828,0.00001193014,0.0000320356,0.00249843,0.04724491,0.4146425,0.5320985,0.001114203,0.0005020134],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5637192,0.0003097064,0.1724328,0.1199223,0.02624406,0.007243067,0.0001246073,0.001399285,0.108605],"genre_scores_gemma":[0.9952697,0.0000272538,0.00151009,0.002637191,0.00008143917,0.0001221079,3.793124e-7,0.00001813412,0.0003336837],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4315505,"threshold_uncertainty_score":0.9999924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06390683959934182,"score_gpt":0.3183797135092722,"score_spread":0.2544728739099303,"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."}}