{"id":"https://openalex.org/W3005055439","doi":"https://doi.org/10.1145/3375627.3375860","title":"An Invitation to System-wide Algorithmic Fairness","display_name":"An Invitation to System-wide Algorithmic Fairness","publication_year":2020,"publication_date":"2020-02-05","ids":{"openalex":"https://openalex.org/W3005055439","doi":"https://doi.org/10.1145/3375627.3375860","mag":"3005055439"},"language":"en","primary_location":{"id":"doi:10.1145/3375627.3375860","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3375627.3375860","pdf_url":null,"source":{"id":"https://openalex.org/S5407048695","display_name":"Proceedings of the AAAI/ACM Conference on AI Ethics and Society","issn_l":"3065-8365","issn":["3065-8365"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049711621","display_name":"Efr\u00e9n Cruz Cort\u00e9s","orcid":"https://orcid.org/0000-0002-2062-6444"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Efr\u00e9n Cruz Cort\u00e9s","raw_affiliation_strings":["The Pennsylvania State University, State College, PA, USA"],"affiliations":[{"raw_affiliation_string":"The Pennsylvania State University, State College, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100625252","display_name":"Debashis Ghosh","orcid":"https://orcid.org/0000-0001-6618-1316"},"institutions":[{"id":"https://openalex.org/I51713134","display_name":"University of Colorado Anschutz Medical Campus","ror":"https://ror.org/03wmf1y16","country_code":"US","type":"education","lineage":["https://openalex.org/I51713134"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Debashis Ghosh","raw_affiliation_strings":["University of Colorado Anschutz, Aurora, CO, USA"],"affiliations":[{"raw_affiliation_string":"University of Colorado Anschutz, Aurora, CO, USA","institution_ids":["https://openalex.org/I51713134"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5049711621"],"corresponding_institution_ids":["https://openalex.org/I130769515"],"apc_list":null,"apc_paid":null,"fwci":1.0263,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.79961988,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"235","last_page":"241"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.9850000143051147,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.9850000143051147,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10410","display_name":"COVID-19 epidemiological studies","score":0.9805999994277954,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7848609685897827},{"id":"https://openalex.org/keywords/scope","display_name":"Scope (computer science)","score":0.6323147416114807},{"id":"https://openalex.org/keywords/limit","display_name":"Limit (mathematics)","score":0.6207960844039917},{"id":"https://openalex.org/keywords/incentive","display_name":"Incentive","score":0.5903286337852478},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5895811319351196},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.5875170230865479},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5714216828346252},{"id":"https://openalex.org/keywords/phenomenon","display_name":"Phenomenon","score":0.4979555606842041},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4695813059806824},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4478057622909546},{"id":"https://openalex.org/keywords/order","display_name":"Order (exchange)","score":0.4324916899204254},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36948060989379883},{"id":"https://openalex.org/keywords/microeconomics","display_name":"Microeconomics","score":0.14004048705101013}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7848609685897827},{"id":"https://openalex.org/C2778012447","wikidata":"https://www.wikidata.org/wiki/Q1034415","display_name":"Scope (computer science)","level":2,"score":0.6323147416114807},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.6207960844039917},{"id":"https://openalex.org/C29122968","wikidata":"https://www.wikidata.org/wiki/Q1414816","display_name":"Incentive","level":2,"score":0.5903286337852478},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5895811319351196},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.5875170230865479},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5714216828346252},{"id":"https://openalex.org/C50335755","wikidata":"https://www.wikidata.org/wiki/Q483247","display_name":"Phenomenon","level":2,"score":0.4979555606842041},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4695813059806824},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4478057622909546},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.4324916899204254},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36948060989379883},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.14004048705101013},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3375627.3375860","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3375627.3375860","pdf_url":null,"source":{"id":"https://openalex.org/S5407048695","display_name":"Proceedings of the AAAI/ACM Conference on AI Ethics and Society","issn_l":"3065-8365","issn":["3065-8365"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.5199999809265137},{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.41999998688697815}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W567313566","https://openalex.org/W641821605","https://openalex.org/W761179351","https://openalex.org/W1546264052","https://openalex.org/W1597666737","https://openalex.org/W1977578104","https://openalex.org/W1992465642","https://openalex.org/W2097098436","https://openalex.org/W2121863487","https://openalex.org/W2134553571","https://openalex.org/W2143117649","https://openalex.org/W2145736592","https://openalex.org/W2159181069","https://openalex.org/W2167951823","https://openalex.org/W2170588867","https://openalex.org/W2529251251","https://openalex.org/W2573660794","https://openalex.org/W2594690992","https://openalex.org/W2599025709","https://openalex.org/W2759566063","https://openalex.org/W2785011159","https://openalex.org/W2799250077","https://openalex.org/W2801890059","https://openalex.org/W2887143297","https://openalex.org/W2896721250","https://openalex.org/W2896833840","https://openalex.org/W2897702578","https://openalex.org/W2919639266","https://openalex.org/W2963659948","https://openalex.org/W2964031043","https://openalex.org/W3122207400","https://openalex.org/W3126362025","https://openalex.org/W4214717370","https://openalex.org/W4239315880","https://openalex.org/W6618788862"],"related_works":["https://openalex.org/W4300450609","https://openalex.org/W4386931570","https://openalex.org/W2391010541","https://openalex.org/W2357367123","https://openalex.org/W4388930439","https://openalex.org/W2387276901","https://openalex.org/W2385953334","https://openalex.org/W2351303360","https://openalex.org/W3134118520","https://openalex.org/W2349223072"],"abstract_inverted_index":{"We":[0,87],"propose":[1,89],"a":[2,35,93,109,116],"framework":[3],"for":[4,80,101,107],"analyzing":[5],"and":[6,32,61,122,145],"evaluating":[7],"system-wide":[8,110],"algorithmic":[9],"fairness.":[10],"The":[11],"core":[12],"idea":[13],"is":[14],"to":[15,21,34,44,55,71,97,130,148],"use":[16,72],"simulation":[17],"techniques":[18],"in":[19],"order":[20],"extend":[22],"the":[23,47,50,69,134],"scope":[24],"of":[25,37,83,119,133],"current":[26],"fairness":[27],"assessments":[28],"by":[29],"incorporating":[30],"context":[31],"feedback":[33],"phenomenon":[36],"interest.":[38],"By":[39],"doing":[40],"so,":[41],"we":[42,67,113,126,136],"expect":[43],"better":[45],"understand":[46],"interaction":[48],"among":[49],"social":[51],"behavior":[52],"giving":[53],"rise":[54],"discrimination,":[56],"automated":[57],"decision":[58],"making":[59],"tools,":[60],"fairness-inspired":[62],"statistical":[63],"constraints.":[64],"In":[65],"particular,":[66],"invite":[68],"community":[70],"agent":[73],"based":[74],"models":[75],"as":[76,143],"an":[77,105],"explanatory":[78],"tool":[79],"causal":[81],"mechanisms":[82],"population":[84],"level":[85],"properties.":[86],"also":[88],"embedding":[90],"these":[91],"into":[92],"reinforcement":[94],"learning":[95],"algorithm":[96],"find":[98],"optimal":[99],"actions":[100],"meaningful":[102],"change.":[103],"As":[104],"incentive":[106],"taking":[108],"approach":[111],",":[112],"show":[114],"through":[115],"simple":[117],"model":[118],"predictive":[120],"policing":[121],"trials":[123],"that":[124],"if":[125],"limit":[127],"our":[128],"attention":[129],"one":[131],"portion":[132],"system,":[135],"may":[137],"determine":[138],"some":[139],"blatantly":[140],"unfair":[141],"practices":[142],"fair,":[144],"be":[146],"blind":[147],"overall":[149],"unfairness.":[150]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}