{"id":"W3152676947","doi":"","title":"Dataset Inference: Ownership Resolution in Machine Learning","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Adversary; Computer science; Inference; Artificial intelligence; Machine learning; Key (lock); Set (abstract data type); Process (computing); Focus (optics); Statistical model; Decision boundary; Computer security","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0008111657,0.0003976585,0.000456357,0.0005068186,0.0002159474,0.0002724196,0.002383183,0.0004345614,0.00008587878],"category_scores_gemma":[0.0006324888,0.0004799502,0.0001361165,0.001186903,0.0001049021,0.0008446277,0.005606073,0.002801702,0.00005148938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004399323,"about_ca_system_score_gemma":0.0003770532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001465504,"about_ca_topic_score_gemma":0.0006310358,"domain_scores_codex":[0.9965373,0.0008819806,0.0003134703,0.001557654,0.0001793842,0.000530174],"domain_scores_gemma":[0.9975998,0.0003214869,0.0003658638,0.001442863,0.0001124095,0.0001575823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002138505,0.00005470877,0.02267061,0.00006485633,0.00002953933,0.0009173782,0.0003608985,0.9508029,0.00001258555,0.02437676,0.00007599014,0.0006124251],"study_design_scores_gemma":[0.0005406906,0.00003032614,0.002993487,0.0002065679,0.0000288767,0.000005850268,0.0001476062,0.989869,0.00001440039,0.004597213,0.001044581,0.000521417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07654391,0.0001760433,0.9210965,0.000221909,0.0006515404,0.0002120301,0.00005935034,0.0002447463,0.0007939743],"genre_scores_gemma":[0.9915617,0.0001978748,0.006146862,0.00008137173,0.00007833911,0.000001144837,0.001437027,0.00002330774,0.0004724036],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9150178,"threshold_uncertainty_score":0.9997652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09053814841093495,"score_gpt":0.2287708281663123,"score_spread":0.1382326797553774,"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."}}