{"id":"W4285059900","doi":"10.1561/1500000079","title":"Fairness in Information Access Systems","year":2022,"lang":"en","type":"article","venue":"Foundations and Trends® in Information Retrieval","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":118,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"Micron Foundation; National Science Foundation","keywords":"Computer science; Information access; Centrality; Personalization; Intersection (aeronautics); Information system; World Wide Web; Data science; Information retrieval; Knowledge management","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001770315,0.00008130672,0.0001293637,0.0009263347,0.00109019,0.001179537,0.0002175635,0.00009202756,0.0003718743],"category_scores_gemma":[0.0004260191,0.00009236104,0.00002889413,0.001855476,0.0001092092,0.009697228,0.0001125909,0.0003385678,0.00001794366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003667109,"about_ca_system_score_gemma":0.0002264922,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008458314,"about_ca_topic_score_gemma":0.002023744,"domain_scores_codex":[0.9984539,0.0001751227,0.000505682,0.00006858869,0.0005617643,0.0002349285],"domain_scores_gemma":[0.9993021,0.0001239725,0.0002164417,0.000094979,0.0001888621,0.00007366617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009238679,0.00007848557,0.01408198,0.00004846266,0.00001466722,0.000001584942,0.1181457,0.005326332,6.690199e-7,0.8102998,0.002125616,0.04978438],"study_design_scores_gemma":[0.001929852,0.0001034644,0.1326799,0.00003403091,0.00001188488,0.000003996405,0.0801155,0.007738511,0.000001938253,0.01010651,0.7668396,0.0004348297],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5617285,0.0001049087,0.001227121,0.01544443,0.002816294,0.0009467598,0.0002617613,0.0001801779,0.41729],"genre_scores_gemma":[0.9988706,0.00007703499,0.00002172866,0.0003336626,0.00004300302,0.00003854232,0.0003357458,0.000002810673,0.000276879],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8001932,"threshold_uncertainty_score":0.9998573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04526192200601802,"score_gpt":0.3783950567943201,"score_spread":0.3331331347883021,"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."}}