{"id":"W3173476916","doi":"10.29173/irie422","title":"Artificial Intelligence, Ethics and International Human Rights Law","year":2021,"lang":"en","type":"article","venue":"The International Review of Information Ethics","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Human rights; Dilemma; International law; Political science; Law; International human rights law; Engineering ethics; Law and economics; Sociology; Engineering; Epistemology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts"],"consensus_categories":[],"category_scores_codex":[0.009224996,0.000098492,0.0001683746,0.00005826478,0.00133342,0.0003119467,0.0006273689,0.0002746175,0.0005635316],"category_scores_gemma":[0.01112598,0.0000780604,0.0001038976,0.0001981293,0.0008195292,0.001258566,0.0001634319,0.00155005,0.00004519806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009147922,"about_ca_system_score_gemma":0.0005378904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001657904,"about_ca_topic_score_gemma":0.004218782,"domain_scores_codex":[0.9971486,0.0004962699,0.0007180976,0.00009718938,0.001395976,0.0001439084],"domain_scores_gemma":[0.9939213,0.001910228,0.0004085767,0.0001689758,0.003514249,0.00007664582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000002992673,0.00001783384,0.000006605346,0.0002064557,0.00003926396,5.739962e-7,0.0174196,0.000004016214,0.00001087208,0.9777471,0.0006932941,0.003851397],"study_design_scores_gemma":[0.00002602805,0.000009389055,0.00003171027,0.00112465,0.00001677822,0.000003088592,0.002441238,0.00004102528,0.0003028445,0.5095773,0.4863398,0.00008608786],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.001103594,0.001727969,0.002632899,0.5048074,0.001769866,0.0002855934,0.00005757046,0.00004501026,0.4875701],"genre_scores_gemma":[0.8588877,0.0650729,0.001206332,0.07282653,0.000776738,0.00001886901,0.0002278055,0.00001047669,0.000972577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8577842,"threshold_uncertainty_score":0.9999667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1679009296172064,"score_gpt":0.472875283374765,"score_spread":0.3049743537575587,"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."}}