{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001336506,0.0005544474,0.0002806131,0.0005037404,0.001361273,0.003761621,0.0007474626,0.001174002,0.006203881],"category_scores_gemma":[0.003235099,0.0002401368,0.0004927478,0.0003009978,0.003719716,0.002485734,0.00259065,0.001737255,0.001569423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001292065,"about_ca_system_score_gemma":0.0008201458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001241187,"about_ca_topic_score_gemma":0.001066267,"domain_scores_codex":[0.9988674,0.0005294714,0.00004682051,0.0001896245,0.0002547923,0.0001119041],"domain_scores_gemma":[0.9988692,0.0004164795,0.00009577946,0.000200787,0.0002416518,0.0001761204],"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":[0.00003347867,0.00005773436,0.001186296,0.0002088155,0.00004644664,0.0001649606,0.001965713,0.004734082,0.004278198,0.919036,0.01700672,0.0512815],"study_design_scores_gemma":[0.00001759327,0.0000456552,0.001293315,0.00007978728,0.00002984955,0.0002501771,0.001286117,0.02174133,0.001438509,0.8475837,0.1261971,0.00003686349],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.04204776,0.006484158,0.3237155,0.03881341,0.001957644,0.0002153047,0.0002284224,0.0007850273,0.5857528],"genre_scores_gemma":[0.8527716,0.003639281,0.07883273,0.005370182,0.0008373487,0.0002767151,0.000259425,0.000191197,0.05782139],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006203881,"threshold_uncertainty_score":0.0207541,"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."}}