{"id":"W3209896493","doi":"","title":"Partial order: Finding Consensus among Uncertain Feature Attributions","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Feature (linguistics); Computer science; Aggregate (composite); Order (exchange); Attribution; Rank (graph theory); Trustworthiness; Artificial intelligence; Machine learning; Post hoc; Psychology; Mathematics; Social psychology; Economics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009553464,0.00125346,0.002445769,0.001805968,0.001614465,0.002752234,0.002466338,0.002519657,0.002360877],"category_scores_gemma":[0.04781351,0.0009956354,0.001615709,0.001477752,0.002711738,0.005402614,0.002707666,0.003180988,0.0003432035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001858128,"about_ca_system_score_gemma":0.002299466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003209919,"about_ca_topic_score_gemma":0.003410029,"domain_scores_codex":[0.9948053,0.002637489,0.0002451435,0.001343725,0.000643151,0.0003252211],"domain_scores_gemma":[0.9499696,0.03929924,0.003282338,0.004187672,0.002086778,0.001174431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008239975,0.0002253267,0.01572481,0.0006178514,0.0009216865,0.0007697892,0.002605186,0.5884978,0.002887074,0.231746,0.005617629,0.149563],"study_design_scores_gemma":[0.00004760512,0.00007715233,0.0009003143,0.00003935339,0.00005479807,0.00007001546,0.0001833762,0.6632845,0.00078499,0.3337907,0.0007364206,0.0000307019],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1088085,0.0003545283,0.8866558,0.001337961,0.00005631075,0.0001396068,0.0002296424,0.000369585,0.002048018],"genre_scores_gemma":[0.8548696,0.0002026942,0.1424557,0.0002857089,0.0001071525,0.0001414579,0.0004773179,0.0001077881,0.00135263],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009553464,"threshold_uncertainty_score":0.05052412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1165002914122213,"score_gpt":0.2276059895553315,"score_spread":0.1111056981431102,"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."}}