{"id":"W4393346483","doi":"10.1016/j.teler.2024.100135","title":"Preserving paradata for accountability of semi-autonomous AI agents in dynamic environments: An archival perspective","year":2024,"lang":"en","type":"article","venue":"Telematics and Informatics Reports","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Perspective (graphical); Accountability; Computer science; Human–computer interaction; Multimedia; Artificial intelligence; Political science; Law","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":[],"consensus_categories":[],"category_scores_codex":[0.001165125,0.0001664055,0.0002844003,0.0001924892,0.00007398208,0.0002887803,0.000418572,0.00005424348,0.000008419626],"category_scores_gemma":[0.000209983,0.0001527779,0.00005511949,0.0002309773,0.00008680607,0.002239063,0.000371503,0.0001583573,0.000002844391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001686771,"about_ca_system_score_gemma":0.0001254961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001267872,"about_ca_topic_score_gemma":0.00005581888,"domain_scores_codex":[0.9979199,0.00002938586,0.001183774,0.0002574706,0.000314488,0.0002949856],"domain_scores_gemma":[0.9985407,0.0001855593,0.0002824395,0.000841764,0.00006160432,0.00008796319],"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":[0.0000651789,0.001906344,0.008945058,0.01215475,0.0004391478,0.0004173953,0.3929982,0.04471923,0.002155346,0.4208281,0.001421792,0.1139494],"study_design_scores_gemma":[0.00004465648,0.000104025,0.001044824,0.0001370679,0.00001086968,0.0000682667,0.00259432,0.9004838,0.001011013,0.09353249,0.0008097496,0.0001588683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4578922,0.0001191724,0.5391304,0.0001812081,0.0002034413,0.0008808897,0.00002109116,0.00006450685,0.001507079],"genre_scores_gemma":[0.9640219,0.0000552057,0.03569311,0.0000758622,0.00001116366,0.00004793334,0.00001796794,0.00001164329,0.00006528391],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8557646,"threshold_uncertainty_score":0.6230102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03171303723347717,"score_gpt":0.3373733910126711,"score_spread":0.3056603537791939,"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."}}