{"id":"W4386014867","doi":"10.1007/978-3-662-67868-8_5","title":"Fairness, Bias and Trust in the Context of Biometric-Enabled Autonomous Decision Support","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face recognition and analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Context (archaeology); Identification (biology); Artificial intelligence; Facial recognition system; Machine learning; Convolutional neural network; Pattern recognition (psychology)","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":[0.002125833,0.0003406236,0.0005901357,0.002925996,0.0001407613,0.0004462039,0.002414666,0.0002256154,0.00002519436],"category_scores_gemma":[0.0003217493,0.0002408134,0.0001432015,0.003608885,0.0005624698,0.0003831587,0.0008350373,0.0004688185,0.00004647328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001019371,"about_ca_system_score_gemma":0.0003601704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008379787,"about_ca_topic_score_gemma":0.0002794223,"domain_scores_codex":[0.9968734,0.00005972543,0.0006490435,0.001024897,0.0009643724,0.0004285823],"domain_scores_gemma":[0.9969294,0.001582652,0.0003062102,0.0008825465,0.0002044112,0.00009478748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003798579,0.00002873512,0.0002573173,0.0000280448,0.000009889081,0.0000843314,0.00103581,0.0008990809,0.00003013266,0.007184981,0.00003820687,0.9903997],"study_design_scores_gemma":[0.001083688,0.0004145348,0.002454156,0.0006937917,0.00003412247,0.0001349419,0.0000136431,0.8182036,0.001277077,0.172126,0.002558682,0.001005823],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001041059,0.0002119879,0.9957079,0.000952629,0.0005800928,0.0002867037,0.00001041008,0.00006973268,0.001139464],"genre_scores_gemma":[0.9092241,0.000385006,0.08773121,0.001949902,0.0001211555,0.00001379725,0.00001234638,0.00003262802,0.0005298487],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9893938,"threshold_uncertainty_score":0.9820083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03550521650450279,"score_gpt":0.2626333337756036,"score_spread":0.2271281172711008,"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."}}