{"id":"W4296448351","doi":"10.1145/3564285","title":"A Multi-Objective Optimization Framework for Multi-Stakeholder Fairness-Aware Recommendation","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Google (Canada); Microsoft (Canada); McGill University","funders":"","keywords":"Computer science; Stakeholder; Pareto principle; Ranking (information retrieval); Multi-objective optimization; Set (abstract data type); Rank (graph theory); Order (exchange); Mathematical optimization; Business; Artificial intelligence; Machine learning; Economics; Mathematics","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":[0.003470051,0.001535765,0.001952262,0.0008003184,0.0008144788,0.001351187,0.002443763,0.002460514,0.004059732],"category_scores_gemma":[0.004103017,0.0008153357,0.001353233,0.001079448,0.0007682358,0.001405202,0.001309712,0.002426604,0.0006629631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001373919,"about_ca_system_score_gemma":0.002143321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01542709,"about_ca_topic_score_gemma":0.01302537,"domain_scores_codex":[0.9985428,0.0005941486,0.00006770861,0.0002981081,0.0003044142,0.0001928716],"domain_scores_gemma":[0.9982919,0.001020105,0.0001365443,0.00009959182,0.000337334,0.0001144938],"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":[0.00004149792,0.00005517535,0.0004119774,0.00006641012,0.00004493757,0.00006477968,0.00004486657,0.9698457,0.0005997421,0.007816579,0.001222727,0.01978563],"study_design_scores_gemma":[0.00000663728,0.00001376069,0.00003841522,0.000004169748,0.000005623304,0.000006397532,0.000004223908,0.9979436,0.00007335489,0.00166862,0.0002310788,0.000003998094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004505774,0.0002679295,0.9931791,0.0001980865,0.00003465224,0.00004795779,0.00005866706,0.0001854591,0.001522313],"genre_scores_gemma":[0.524811,0.0004480494,0.4673045,0.0004457673,0.0001357201,0.0004576495,0.000301998,0.0001522454,0.005943125],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01542709,"threshold_uncertainty_score":0.03067458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09382313345199016,"score_gpt":0.309685478613399,"score_spread":0.2158623451614088,"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."}}