{"id":"W2999204792","doi":"10.1145/3375627.3375875","title":"Investigating the Impact of Inclusion in Face Recognition Training Data on Individual Face Identification","year":2020,"lang":"en","type":"preprint","venue":"Proceedings of the AAAI/ACM Conference on AI Ethics and Society","topic":"Face recognition and analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Facial recognition system; Computer science; Artificial intelligence; Identification (biology); Convolutional neural network; Three-dimensional face recognition; Face Recognition Grand Challenge; Pattern recognition (psychology); Embedding; Machine learning; Face detection","routes":{"ca_aff":true,"ca_fund":true,"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":["open_science","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004614105,0.0002916243,0.0004424112,0.00008619575,0.0005453901,0.000480835,0.004071964,0.0004686677,0.000005128393],"category_scores_gemma":[0.003328681,0.0001933354,0.00027503,0.0006162059,0.0003441564,0.0003721141,0.01203608,0.003271245,0.000002067021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006540018,"about_ca_system_score_gemma":0.0005599436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000203189,"about_ca_topic_score_gemma":0.000009397723,"domain_scores_codex":[0.9972242,0.0001445044,0.0006344896,0.0007897135,0.0009830042,0.0002240468],"domain_scores_gemma":[0.9969485,0.0005454247,0.001120082,0.0006838596,0.0006130772,0.00008900226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000357414,0.0003946807,0.002285975,0.00201728,0.00081721,4.309897e-7,0.7514086,0.001183989,0.05481132,0.03217177,0.003940421,0.1509326],"study_design_scores_gemma":[0.0005564358,0.0001795636,0.00965652,0.003423988,0.0001430584,0.000002375898,0.01705105,0.6419954,0.009887873,0.3165434,0.00001527058,0.0005451214],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8934374,0.0001063091,0.005309446,0.09834626,0.0002137767,0.00101884,0.000532159,0.000090598,0.0009452063],"genre_scores_gemma":[0.994886,0.0007961817,0.002894861,0.001256515,0.00003562671,0.00001771941,0.00008624473,0.00001282782,0.00001402039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7343575,"threshold_uncertainty_score":0.9990283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3155141622354531,"score_gpt":0.3921573419339655,"score_spread":0.07664317969851242,"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."}}