{"id":"W2905103471","doi":"","title":"Group Fairness for Indivisible Goods Allocation","year":2019,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Fair division; Set (abstract data type); Group (periodic table); Mathematical economics; Computer science; Fairness measure; Public good; Nash equilibrium; Max-min fairness; Function (biology); Microeconomics; Mathematics; Resource allocation; Economics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001373349,0.0001356484,0.0002398349,0.0002162226,0.0001098189,0.0001297644,0.0002946666,0.0001134233,0.0008885384],"category_scores_gemma":[0.0003394914,0.0001567164,0.0000854696,0.0002282014,0.00004657852,0.0002098538,0.00002264852,0.0001275684,0.004091005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009559935,"about_ca_system_score_gemma":0.0000463904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000361526,"about_ca_topic_score_gemma":0.00002054847,"domain_scores_codex":[0.9986382,0.00002686777,0.0005796862,0.0004228345,0.0001094078,0.0002230107],"domain_scores_gemma":[0.9990597,0.0002297956,0.0002888509,0.0001814404,0.0001883653,0.00005180924],"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":[0.00004597486,0.00008887183,0.0008625656,0.00002407004,0.00001358563,9.026895e-8,0.0001852683,0.0005021076,0.0003933006,0.993241,0.00005814993,0.004585089],"study_design_scores_gemma":[0.00006106696,0.0001786556,0.001065703,0.0000369716,0.000001667451,7.731155e-7,0.0002000633,0.03475258,0.003368102,0.9570862,0.003022345,0.0002258838],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2958802,0.0001186483,0.5253105,0.002199417,0.003008108,0.001726526,0.0003924384,0.0001738945,0.1711903],"genre_scores_gemma":[0.9975535,0.000006001764,0.0004969417,0.0002416592,0.0001697519,0.00008360733,0.00006125188,0.00001553457,0.001371798],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7016733,"threshold_uncertainty_score":0.9966844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1786167361537452,"score_gpt":0.3224723847717426,"score_spread":0.1438556486179973,"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."}}