{"id":"W2922210341","doi":"10.11141/ia.52.5","title":"What the Machine Saw: some questions on the ethics of computer vision and machine learning to investigate human remains trafficking","year":2019,"lang":"en","type":"article","venue":"Internet Archaeology","topic":"Forensic Anthropology and Bioarchaeology Studies","field":"Arts and Humanities","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Social Sciences and Humanities Research Council of Canada; Carleton University","keywords":"False positive paradox; Artificial intelligence; Computer science; Rhetoric; Social media; Classifier (UML); True positive rate; Data science; World Wide Web","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0007248961,0.0001911166,0.0002984271,0.0001139657,0.0002966935,0.00002343095,0.0002921654,0.0001152576,0.000387311],"category_scores_gemma":[0.0001050795,0.00009847397,0.0000668901,0.00003042507,0.01944803,0.00009249837,0.0006708318,0.0009925802,0.00006350672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001310794,"about_ca_system_score_gemma":0.00001438119,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003145982,"about_ca_topic_score_gemma":0.0595402,"domain_scores_codex":[0.9984665,0.0005904412,0.0002737581,0.0002819863,0.0001181962,0.0002691221],"domain_scores_gemma":[0.9983301,0.001201396,0.0001215941,0.0002269585,0.00007939498,0.00004051535],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005423517,0.00002118398,0.004196986,0.00001627057,0.00008547087,0.000005860964,0.2247855,0.0000582727,0.00002985106,0.7697302,0.0003386749,0.0006775185],"study_design_scores_gemma":[0.002454181,0.01957148,0.01919104,0.001916205,0.0002734727,0.0003167427,0.1258639,0.02213361,0.001556607,0.2367689,0.5685057,0.001448141],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8449923,0.0007594507,0.00006760925,0.1525841,0.00105508,0.0002393576,0.000006138998,0.00004220248,0.0002537313],"genre_scores_gemma":[0.989468,0.0003049131,0.00007064799,0.006836617,0.0002214274,0.0000106177,0.0000136946,0.00001474903,0.003059287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.568167,"threshold_uncertainty_score":0.9832205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04399954373157969,"score_gpt":0.2946263926614663,"score_spread":0.2506268489298866,"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."}}