{"id":"W4366547999","doi":"10.1145/3544549.3573794","title":"GenAICHI 2023: Generative AI and HCI at CHI 2023","year":2023,"lang":"en","type":"article","venue":"","topic":"Creativity in Education and Neuroscience","field":"Psychology","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Microsoft (Canada)","funders":"","keywords":"Generative grammar; Computer science; Generative Design; Generative model; Human–computer interaction; Cognitive science; Artificial intelligence; Psychology; 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":[0.008522557,0.001705395,0.0009956994,0.0012118,0.002547098,0.007550149,0.00242192,0.004898407,0.09267005],"category_scores_gemma":[0.006235388,0.0007522932,0.001350257,0.001728995,0.002529513,0.003812586,0.005157149,0.007191697,0.02562774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00328934,"about_ca_system_score_gemma":0.003397553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00835494,"about_ca_topic_score_gemma":0.01298652,"domain_scores_codex":[0.9970198,0.001316819,0.00005572425,0.0003217949,0.0008545721,0.0004311719],"domain_scores_gemma":[0.9964761,0.001264789,0.00004033251,0.0002708588,0.0006586514,0.001289169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003665099,0.0001841252,0.0006236322,0.0004450348,0.00005051837,0.0004264092,0.004874808,0.001217626,0.005212749,0.04357924,0.8085304,0.1344889],"study_design_scores_gemma":[0.0001379873,0.0002258631,0.001079602,0.0004195158,0.00002204405,0.0003779286,0.00154927,0.003012988,0.002054785,0.03219854,0.9588397,0.00008186432],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.02695638,0.03453683,0.2387002,0.07949387,0.04022171,0.002032045,0.003507772,0.008786202,0.565765],"genre_scores_gemma":[0.1061644,0.01286215,0.07463045,0.008823864,0.007114897,0.002868306,0.005812063,0.006513226,0.7752106],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.09267005,"threshold_uncertainty_score":0.3100122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05438084628928203,"score_gpt":0.3957586629603024,"score_spread":0.3413778166710204,"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."}}