{"id":"W4292662922","doi":"10.5204/lthj.2332","title":"The Promise and Perils of International Human Rights Law for AI Governance","year":2022,"lang":"en","type":"article","venue":"Law Technology and Humans","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Human rights; Realm; Corporate governance; Political science; Law and economics; International human rights law; Public international law; Law; Soft law; Global governance; International law; Sociology; Economics; Management; Politics","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0004253441,0.00004149305,0.00007736385,0.00001590417,0.004685711,0.00005163053,0.0002334012,0.00008820367,0.00003456644],"category_scores_gemma":[0.00003627129,0.00003400916,0.00002130831,0.0000508035,0.00190036,0.00009239576,0.00009674206,0.0001986039,1.542794e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002303036,"about_ca_system_score_gemma":0.00001874717,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001602033,"about_ca_topic_score_gemma":0.04137954,"domain_scores_codex":[0.9995266,0.00002922234,0.00009183332,0.0001028342,0.0001234388,0.0001261153],"domain_scores_gemma":[0.999689,0.0000767924,0.00006782275,0.00007585716,0.00007164506,0.00001889452],"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":[0.000007393519,0.00001752109,0.000189945,0.00000267305,0.00001432094,6.010948e-7,0.002848198,9.235183e-8,0.0001308507,0.9963047,0.000251848,0.0002318945],"study_design_scores_gemma":[0.0001175853,0.00005435929,0.0001330454,0.000002375753,0.000003778943,3.214857e-7,0.0007338561,9.552284e-7,0.00010779,0.5278991,0.4709182,0.00002859679],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7589492,0.001018873,0.000009368119,0.08847963,0.0003519719,0.0004765895,0.0000713309,0.00008831373,0.1505547],"genre_scores_gemma":[0.9974741,0.00007398894,0.00004061468,0.0004016728,0.00005027639,0.00004427594,0.00000149122,0.000003165464,0.001910456],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4706663,"threshold_uncertainty_score":0.99661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01783856887130554,"score_gpt":0.3441403650849708,"score_spread":0.3263017962136652,"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."}}