{"id":"W3046399757","doi":"10.1109/mc.2020.2996587","title":"A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence","year":2020,"lang":"en","type":"article","venue":"Computer","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":376,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Intellect; Computer science; Human intelligence; Artificial intelligence; Set (abstract data type); Artificial intelligence, situated approach; Symbolic artificial intelligence; Marketing and artificial intelligence; Applications of artificial intelligence; Focus (optics); Artificial general intelligence; Computational intelligence; Cognitive science; Intelligent decision support system; Psychology; Epistemology","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","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002400591,0.0004806597,0.0005502685,0.0005769439,0.001450313,0.001241064,0.001857038,0.0001330527,0.00005660547],"category_scores_gemma":[0.0004816451,0.0004499886,0.0001081327,0.003057108,0.0006165734,0.001256036,0.001337119,0.0007604781,0.0001426527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001564826,"about_ca_system_score_gemma":0.0005217457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000543879,"about_ca_topic_score_gemma":0.0000652866,"domain_scores_codex":[0.9946885,0.0006508476,0.0008682264,0.001673446,0.0008148083,0.00130424],"domain_scores_gemma":[0.9951158,0.001914727,0.0002286746,0.0008131237,0.0014676,0.0004600619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001028292,0.0003073488,0.00005813933,0.0002356098,0.0001521305,0.0003839791,0.01650165,0.007188899,0.004254138,0.722998,0.008020342,0.2388714],"study_design_scores_gemma":[0.0001433888,0.004382981,0.00002684751,0.0002504231,0.00001895913,0.00004789158,0.004584694,0.6966619,0.2171628,0.06611322,0.009756108,0.0008507131],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01569683,0.0003138099,0.9776158,0.003193222,0.0002601634,0.001549843,0.00001220279,0.0003238055,0.001034269],"genre_scores_gemma":[0.7376114,0.00004583138,0.2604147,0.0008906846,0.0004714374,0.0002582273,0.000009080054,0.00006227568,0.0002363775],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7219146,"threshold_uncertainty_score":0.9998497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1797266306899354,"score_gpt":0.3685186475705217,"score_spread":0.1887920168805863,"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."}}