{"id":"https://openalex.org/W2946647999","doi":"https://doi.org/10.1145/3308560.3317590","title":"Managing Bias in AI","display_name":"Managing Bias in AI","publication_year":2019,"publication_date":"2019-05-13","ids":{"openalex":"https://openalex.org/W2946647999","doi":"https://doi.org/10.1145/3308560.3317590","mag":"2946647999"},"language":"en","primary_location":{"id":"doi:10.1145/3308560.3317590","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3308560.3317590","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of The 2019 World Wide Web Conference","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3308560.3317590","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043651755","display_name":"Drew Roselli","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Drew Roselli","raw_affiliation_strings":["ParallelM"],"affiliations":[{"raw_affiliation_string":"ParallelM","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070197299","display_name":"Jeanna Matthews","orcid":"https://orcid.org/0000-0001-5955-0996"},"institutions":[{"id":"https://openalex.org/I16944753","display_name":"Clarkson University","ror":"https://ror.org/03rwgpn18","country_code":"US","type":"education","lineage":["https://openalex.org/I16944753"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jeanna Matthews","raw_affiliation_strings":["Clarkson Univ"],"affiliations":[{"raw_affiliation_string":"Clarkson Univ","institution_ids":["https://openalex.org/I16944753"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056708347","display_name":"Nisha Talagala","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nisha Talagala","raw_affiliation_strings":["ParallelM"],"affiliations":[{"raw_affiliation_string":"ParallelM","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5043651755"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.2025,"has_fulltext":false,"cited_by_count":192,"citation_normalized_percentile":{"value":0.96366423,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"539","last_page":"544"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.9966999888420105,"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/T11891","display_name":"Big Data and Business Intelligence","score":0.9675999879837036,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7684787511825562},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5649734139442444},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.5359699726104736},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4797552824020386},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4676033556461334},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44384831190109253},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3670019507408142},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.36658865213394165}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7684787511825562},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5649734139442444},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.5359699726104736},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4797552824020386},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4676033556461334},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44384831190109253},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3670019507408142},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.36658865213394165},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3308560.3317590","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3308560.3317590","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of The 2019 World Wide Web Conference","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3308560.3317590","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3308560.3317590","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of The 2019 World Wide Web Conference","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1570448133","https://openalex.org/W2006447892","https://openalex.org/W2282821441","https://openalex.org/W2350778671","https://openalex.org/W2405607598","https://openalex.org/W2483215953","https://openalex.org/W2493343568","https://openalex.org/W2563852449","https://openalex.org/W2573660794","https://openalex.org/W2584997055","https://openalex.org/W2743948853","https://openalex.org/W2745573985","https://openalex.org/W2764072425","https://openalex.org/W2788481061","https://openalex.org/W2793931959","https://openalex.org/W2807251972","https://openalex.org/W2892939510","https://openalex.org/W2898147565","https://openalex.org/W2904000786","https://openalex.org/W2963777610","https://openalex.org/W2966207845","https://openalex.org/W2991044292","https://openalex.org/W3125143440","https://openalex.org/W4220820301","https://openalex.org/W6740085662","https://openalex.org/W6751917733","https://openalex.org/W6988977658"],"related_works":["https://openalex.org/W1585007175","https://openalex.org/W2382521049","https://openalex.org/W2144385241","https://openalex.org/W2165950148","https://openalex.org/W4253593777","https://openalex.org/W2951497643","https://openalex.org/W4300101996","https://openalex.org/W3101625811","https://openalex.org/W2338854850","https://openalex.org/W2184239527"],"abstract_inverted_index":{"Recent":[0],"awareness":[1],"of":[2,5,39,74,87,107,142],"the":[3,11,21,28,40,93,97,105,140],"impacts":[4],"bias":[6,143],"in":[7,27,117],"AI":[8,57,98],"algorithms":[9,22,33,41],"raises":[10],"risk":[12],"for":[13,110],"companies":[14,77],"to":[15,50,80,91,104,130,138],"deploy":[16],"such":[17],"algorithms,":[18],"especially":[19],"because":[20,56],"may":[23,46,123],"not":[24,47],"be":[25,48,124,136],"explainable":[26],"same":[29],"way":[30],"that":[31,76,101,114],"non-AI":[32],"are.":[34],"Even":[35],"with":[36],"careful":[37],"review":[38],"and":[42,82,112],"data":[43],"sets,":[44],"it":[45],"possible":[49],"delete":[51],"all":[52],"unwanted":[53],"bias,":[54],"particularly":[55],"systems":[58],"learn":[59],"from":[60],"historical":[61,65],"data,":[62],"which":[63],"encodes":[64],"biases.":[66],"In":[67],"this":[68,131],"paper,":[69],"we":[70],"propose":[71],"a":[72],"set":[73],"processes":[75],"can":[78,135],"use":[79],"mitigate":[81],"manage":[83],"three":[84],"general":[85],"classes":[86],"bias:":[88],"those":[89,100,113],"related":[90],"mapping":[92],"business":[94],"intent":[95],"into":[96],"implementation,":[99],"arise":[102],"due":[103],"distribution":[106],"samples":[108],"used":[109,137],"training,":[111],"are":[115],"present":[116],"individual":[118],"input":[119],"samples.":[120],"While":[121],"there":[122],"no":[125],"simple":[126],"or":[127],"complete":[128],"solution":[129],"issue,":[132],"best":[133],"practices":[134],"reduce":[139],"effects":[141],"on":[144],"algorithmic":[145],"outcomes.":[146]},"counts_by_year":[{"year":2026,"cited_by_count":10},{"year":2025,"cited_by_count":54},{"year":2024,"cited_by_count":51},{"year":2023,"cited_by_count":42},{"year":2022,"cited_by_count":19},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-15T08:11:43.952461","created_date":"2025-10-10T00:00:00"}