{"id":"https://openalex.org/W3193160514","doi":"https://doi.org/10.1145/3461702.3462614","title":"Can We Obtain Fairness For Free?","display_name":"Can We Obtain Fairness For Free?","publication_year":2021,"publication_date":"2021-07-21","ids":{"openalex":"https://openalex.org/W3193160514","doi":"https://doi.org/10.1145/3461702.3462614","mag":"3193160514"},"language":"en","primary_location":{"id":"doi:10.1145/3461702.3462614","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3461702.3462614","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3461702.3462614","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3461702.3462614","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101830965","display_name":"Rashidul Islam","orcid":"https://orcid.org/0000-0001-5276-5708"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Rashidul Islam","raw_affiliation_strings":["University of Maryland, Baltimore County, Baltimore, MD, USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048111120","display_name":"Shimei Pan","orcid":"https://orcid.org/0000-0002-5989-8543"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shimei Pan","raw_affiliation_strings":["University of Maryland, Baltimore County, Baltimore, MD, USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010326834","display_name":"James R. Foulds","orcid":"https://orcid.org/0000-0003-0935-4182"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James R. Foulds","raw_affiliation_strings":["University of Maryland, Baltimore County, Baltimore, MD, USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101830965"],"corresponding_institution_ids":["https://openalex.org/I79272384"],"apc_list":null,"apc_paid":null,"fwci":4.0238,"has_fulltext":true,"cited_by_count":21,"citation_normalized_percentile":{"value":0.93969945,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"586","last_page":"596"},"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":0.9990000128746033,"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":0.9990000128746033,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9290000200271606,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.813561201095581},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7931761741638184},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7838937044143677},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6039254665374756},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.553983211517334},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.45873183012008667},{"id":"https://openalex.org/keywords/fairness-measure","display_name":"Fairness measure","score":0.45583653450012207},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.42965370416641235},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.11941438913345337},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.0768897533416748},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.07358601689338684}],"concepts":[{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.813561201095581},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7931761741638184},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7838937044143677},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6039254665374756},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.553983211517334},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.45873183012008667},{"id":"https://openalex.org/C11867375","wikidata":"https://www.wikidata.org/wiki/Q5430671","display_name":"Fairness measure","level":4,"score":0.45583653450012207},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.42965370416641235},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.11941438913345337},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0768897533416748},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.07358601689338684},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3461702.3462614","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3461702.3462614","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3461702.3462614","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society","raw_type":"proceedings-article"},{"id":"pmh:oai:mdsoar.org:11603/25535","is_oa":false,"landing_page_url":"http://hdl.handle.net/11603/25535","pdf_url":null,"source":{"id":"https://openalex.org/S4306402556","display_name":"Maryland Shared Open Access Repository (USMAI Consortium)","issn_l":null,"issn":null,"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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"},{"id":"doi:10.13016/m2f6gz-p0wh","is_oa":true,"landing_page_url":"https://doi.org/10.13016/m2f6gz-p0wh","pdf_url":null,"source":{"id":"https://openalex.org/S4306402644","display_name":"Digital Repository at the University of Maryland (University of Maryland College Park)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.1145/3461702.3462614","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3461702.3462614","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3461702.3462614","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7099999785423279}],"awards":[{"id":"https://openalex.org/G2794811839","display_name":"AI-DCL: Fairness for the Allocation of Healthcare Resources","funder_award_id":"1927486","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3820112067","display_name":"CRII: RI: Bayesian Models for Fairness, and Fairness for Bayesian Models","funder_award_id":"1850023","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4451808935","display_name":null,"funder_award_id":"60NANB18D227","funder_id":"https://openalex.org/F4320332178","funder_display_name":"National Institute of Standards and Technology"},{"id":"https://openalex.org/G5490465407","display_name":"CAREER: Fair Artificial Intelligence for Intelligent Humans: Removing the Barriers to Deployment of Fair AI Technologies","funder_award_id":"2046381","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8023464908","display_name":null,"funder_award_id":"IIS2046381, IIS1850023, IIS1927486","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306111","display_name":"U.S. Department of Commerce","ror":"https://ror.org/04chq2495"},{"id":"https://openalex.org/F4320332178","display_name":"National Institute of Standards and Technology","ror":"https://ror.org/05xpvk416"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3193160514.pdf","grobid_xml":"https://content.openalex.org/works/W3193160514.grobid-xml"},"referenced_works_count":44,"referenced_works":["https://openalex.org/W64581525","https://openalex.org/W114517082","https://openalex.org/W584462734","https://openalex.org/W608314793","https://openalex.org/W1819662813","https://openalex.org/W2020034787","https://openalex.org/W2084341220","https://openalex.org/W2100960835","https://openalex.org/W2130486630","https://openalex.org/W2162670686","https://openalex.org/W2166454173","https://openalex.org/W2530395818","https://openalex.org/W2546563948","https://openalex.org/W2599025709","https://openalex.org/W2753845591","https://openalex.org/W2768894107","https://openalex.org/W2807251972","https://openalex.org/W2810290439","https://openalex.org/W2899771611","https://openalex.org/W2901823434","https://openalex.org/W2904539038","https://openalex.org/W2943927551","https://openalex.org/W2945445411","https://openalex.org/W2946016480","https://openalex.org/W2950018712","https://openalex.org/W2951476734","https://openalex.org/W2953487715","https://openalex.org/W2963116854","https://openalex.org/W2963611658","https://openalex.org/W2963808661","https://openalex.org/W2970971581","https://openalex.org/W2990453483","https://openalex.org/W3023309920","https://openalex.org/W3031684522","https://openalex.org/W3032340379","https://openalex.org/W3034658884","https://openalex.org/W3093291758","https://openalex.org/W3120740533","https://openalex.org/W3123374861","https://openalex.org/W3156200279","https://openalex.org/W3197709042","https://openalex.org/W4295312788","https://openalex.org/W4310936740","https://openalex.org/W6638208828"],"related_works":["https://openalex.org/W2770234245","https://openalex.org/W96612179","https://openalex.org/W4229499248","https://openalex.org/W2566006169","https://openalex.org/W1567818861","https://openalex.org/W2378211422","https://openalex.org/W2987774938","https://openalex.org/W4256492088","https://openalex.org/W632915154","https://openalex.org/W2055733372"],"abstract_inverted_index":{"There":[0],"is":[1,51,102,136],"growing":[2],"awareness":[3],"that":[4,55,100,134],"AI":[5,35,171],"and":[6,19],"machine":[7],"learning":[8,125],"systems":[9],"can":[10],"in":[11,17,43,60,151],"some":[12,142],"cases":[13],"learn":[14],"to":[15,39,94,107,138,141],"behave":[16],"unfair":[18],"discriminatory":[20],"ways":[21],"with":[22,144,173],"harmful":[23],"consequences.":[24],"However,":[25],"despite":[26],"an":[27,70,149],"enormous":[28],"amount":[29],"of":[30,47,123,130,169],"research,":[31],"techniques":[32],"for":[33],"ensuring":[34],"fairness":[36,56,113,140],"have":[37],"yet":[38],"see":[40],"widespread":[41],"deployment":[42,168],"real":[44],"systems.":[45],"One":[46],"the":[48,52,95,121,167],"main":[49],"barriers":[50],"conventional":[53,96],"wisdom":[54],"brings":[57],"a":[58,78,110,128,155,163],"cost":[59],"predictive":[61,116,152],"performance":[62,153],"metrics":[63],"such":[64],"as":[65],"accuracy":[66],"which":[67],"could":[68],"affect":[69],"organization's":[71],"bottom-line.":[72],"In":[73,92],"this":[74,82],"paper":[75],"we":[76,98],"take":[77],"closer":[79],"look":[80],"at":[81],"concern.":[83],"Clearly":[84],"fairness/performance":[85],"trade-offs":[86],"exist,":[87],"but":[88],"are":[89],"they":[90],"inevitable?":[91],"contrast":[93],"wisdom,":[97],"find":[99],"it":[101,135],"frequently":[103],"possible,":[104],"indeed":[105],"straightforward,":[106],"improve":[108,139],"on":[109,127],"trained":[111],"model's":[112],"without":[114],"sacrificing":[115],"performance.":[117],"We":[118],"systematically":[119],"study":[120],"behavior":[122],"fair":[124,170],"algorithms":[126],"range":[129],"benchmark":[131],"datasets,":[132],"showing":[133],"possible":[137],"degree":[143],"no":[145],"loss":[146],"(or":[147],"even":[148],"improvement)":[150],"via":[154],"sensible":[156],"hyper-parameter":[157],"selection":[158],"strategy.":[159],"Our":[160],"results":[161],"reveal":[162],"pathway":[164],"toward":[165],"increasing":[166],"methods,":[172],"potentially":[174],"substantial":[175],"positive":[176],"real-world":[177],"impacts.":[178]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":6}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
