{"id":"W7133098856","doi":"","title":"Natural Language Processing (NLP) For Ethical Artificial Intelligence (AI)","year":2025,"lang":"en","type":"dissertation","venue":"Trepo - Institutional Repository of Tampere University","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Latent Dirichlet allocation; Topic model; Perception; Ethical issues; Thematic analysis; Identification (biology); Natural language understanding; Software deployment","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":[0.008416081,0.0008505277,0.0008965531,0.004401059,0.001932281,0.007092976,0.001332673,0.002351513,0.01810395],"category_scores_gemma":[0.03417918,0.0005481179,0.001379246,0.004812029,0.002603531,0.008049151,0.004221659,0.004772988,0.01250853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001866775,"about_ca_system_score_gemma":0.003978326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001995041,"about_ca_topic_score_gemma":0.001848443,"domain_scores_codex":[0.9918078,0.004525854,0.0007558707,0.00127732,0.001459176,0.0001739477],"domain_scores_gemma":[0.9640014,0.02834003,0.002029776,0.002783402,0.002461341,0.0003840367],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001369367,0.0001700505,0.002906784,0.004244692,0.0001450849,0.0007174766,0.002945121,0.003887322,0.01148433,0.2102102,0.1297717,0.6333803],"study_design_scores_gemma":[0.00004203178,0.0001099936,0.003974672,0.002175587,0.00005728934,0.001036622,0.002886577,0.04124701,0.005523907,0.3878922,0.5549403,0.0001139487],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01483598,0.02576305,0.8149869,0.04658652,0.004383771,0.00171837,0.0150205,0.004702234,0.07200273],"genre_scores_gemma":[0.1442206,0.02624185,0.7655262,0.009356193,0.003805107,0.004382799,0.02276151,0.00136581,0.02234],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01810395,"threshold_uncertainty_score":0.06056374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03235354115550524,"score_gpt":0.3697557667082658,"score_spread":0.3374022255527606,"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."}}