{"id":"W4387758950","doi":"10.59172/2667-9876/2023-2/45-53","title":"Towards the Status of Classification of Artificial Intelligence as a Subject of Law","year":2021,"lang":"en","type":"article","venue":"Iustitia","topic":"Legal and Policy Issues","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Digital transformation; Scope (computer science); Context (archaeology); Subject (documents); Computer science; Process (computing); Meaning (existential); Data science; Jurisprudence; Artificial intelligence; Political science; Law; World Wide Web; Epistemology","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":[0.0002293196,0.00003773766,0.0001046665,0.00001742043,0.00006909908,0.00001186125,0.000134627,0.00004393589,0.000316975],"category_scores_gemma":[0.0004290048,0.00002840457,0.00004985334,0.0002438053,0.0005227772,0.00005964693,0.00002163797,0.00005082175,0.00001065546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002002463,"about_ca_system_score_gemma":0.0004856865,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05655666,"about_ca_topic_score_gemma":0.01220928,"domain_scores_codex":[0.9992058,0.0001288969,0.0002027703,0.00007354313,0.0002325197,0.0001564361],"domain_scores_gemma":[0.9993806,0.0001229905,0.0001064771,0.0001317891,0.0002171409,0.00004103248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001057502,0.00003835932,0.0003057194,0.00001929885,0.000009556313,8.296517e-7,0.02136442,0.000006592703,0.00745308,0.959168,0.00008614163,0.0115375],"study_design_scores_gemma":[0.00005438392,0.0001193329,0.007279406,0.00007514323,0.00005837293,0.000001052057,0.02414094,0.00007612275,0.6528757,0.1262627,0.1889285,0.0001283512],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5298203,0.0005641992,0.0002996133,0.002433969,0.0002786688,0.0001221926,0.00007471467,0.00001453819,0.4663919],"genre_scores_gemma":[0.9992023,0.0001686248,0.0001162482,0.00005638691,0.0001029535,0.000002217845,0.000003095037,0.000002429069,0.0003458227],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8329052,"threshold_uncertainty_score":0.9497258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1304900327669395,"score_gpt":0.4068958157909387,"score_spread":0.2764057830239992,"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."}}