{"id":"W4287690580","doi":"10.48550/arxiv.2008.04520","title":"Montreal AI Ethics Institute's (MAIEI) Submission to the World\\n Intellectual Property Organization (WIPO) Conversation on Intellectual\\n Property (IP) and Artificial Intelligence (AI) Second Session","year":2020,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Law, AI, and Intellectual Property","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Intellectual property; Conversation; Session (web analytics); Law; Political science; Sociology; Business; Computer science; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.001845005,0.001714157,0.001335978,0.001061838,0.002748042,0.001788031,0.004225394,0.001370971,0.002588639],"category_scores_gemma":[0.007496031,0.001065101,0.0003865387,0.006359416,0.001817619,0.002283498,0.0056962,0.005863254,0.001695354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001295501,"about_ca_system_score_gemma":0.002667802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001440528,"about_ca_topic_score_gemma":0.00386941,"domain_scores_codex":[0.9894827,0.002107928,0.001550163,0.004660668,0.0008419448,0.001356566],"domain_scores_gemma":[0.9912243,0.001910434,0.0007258856,0.002369982,0.002507188,0.001262163],"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":[0.01073402,0.002987686,0.0003609829,0.00178913,0.001320367,0.0007062576,0.3619654,0.2096844,0.008966256,0.08097418,0.1037898,0.2167216],"study_design_scores_gemma":[0.0005643482,0.002118635,0.00007209783,0.001186825,0.0002795424,0.00004502782,0.002314509,0.8931906,0.03077398,0.00802213,0.05927468,0.002157571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04209934,0.00008867742,0.9210483,0.02528064,0.003649688,0.003922208,0.00005602435,0.0005409216,0.003314159],"genre_scores_gemma":[0.974401,0.001272379,0.0004290084,0.00926066,0.0007130281,0.00001131422,0.0001301197,0.0001494322,0.01363305],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9323016,"threshold_uncertainty_score":0.9999254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1428389202933342,"score_gpt":0.2325991303194648,"score_spread":0.08976021002613066,"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."}}