{"id":"https://openalex.org/W2952676795","doi":"https://doi.org/10.48550/arxiv.1810.06755","title":"Discovering Fair Representations in the Data Domain","display_name":"Discovering Fair Representations in the Data Domain","publication_year":2018,"publication_date":"2018-10-15","ids":{"openalex":"https://openalex.org/W2952676795","doi":"https://doi.org/10.48550/arxiv.1810.06755","mag":"2952676795"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:1810.06755","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1810.06755","pdf_url":"https://arxiv.org/pdf/1810.06755","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1810.06755","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021434291","display_name":"Novi Quadrianto","orcid":"https://orcid.org/0000-0001-8819-306X","observed_orcids":["https://orcid.org/0000-0001-8819-306X"]},"institutions":[{"id":"https://openalex.org/I162608824","display_name":"University of Sussex","ror":"https://ror.org/00ayhx656","country_code":"GB","type":"education","lineage":["https://openalex.org/I162608824"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Quadrianto, Novi","raw_affiliation_strings":["\u2021Predictive Analytics Lab (PAL) , University of Sussex , Brighton , United Kingdom \u00a7Department of Computing ,","Univ. of Sussex / HSE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"\u2021Predictive Analytics Lab (PAL) , University of Sussex , Brighton , United Kingdom \u00a7Department of Computing ,","institution_ids":["https://openalex.org/I162608824"]},{"raw_affiliation_string":"Univ. of Sussex / HSE","institution_ids":["https://openalex.org/I162608824"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006827927","display_name":"Viktoriia Sharmanska","orcid":"https://orcid.org/0000-0003-0192-9308","observed_orcids":["https://orcid.org/0000-0003-0192-9308"]},"institutions":[{"id":"https://openalex.org/I162608824","display_name":"University of Sussex","ror":"https://ror.org/00ayhx656","country_code":"GB","type":"education","lineage":["https://openalex.org/I162608824"]},{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Sharmanska, Viktoriia","raw_affiliation_strings":["\u2021Predictive Analytics Lab (PAL) , University of Sussex , Brighton , United Kingdom \u00a7Department of Computing ,","Imperial College London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"\u2021Predictive Analytics Lab (PAL) , University of Sussex , Brighton , United Kingdom \u00a7Department of Computing ,","institution_ids":["https://openalex.org/I162608824"]},{"raw_affiliation_string":"Imperial College London","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088797103","display_name":"Oliver Thomas","orcid":"https://orcid.org/0000-0001-8162-9633","observed_orcids":["https://orcid.org/0000-0001-8162-9633"]},"institutions":[{"id":"https://openalex.org/I162608824","display_name":"University of Sussex","ror":"https://ror.org/00ayhx656","country_code":"GB","type":"education","lineage":["https://openalex.org/I162608824"]},{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Thomas, Oliver","raw_affiliation_strings":["Imperial College London , United Kingdom","University of Sussex,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Imperial College London , United Kingdom","institution_ids":["https://openalex.org/I47508984"]},{"raw_affiliation_string":"University of Sussex,","institution_ids":["https://openalex.org/I162608824"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":28,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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":0.9434000253677368,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.9434000253677368,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.014999999664723873,"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/T11448","display_name":"Face recognition and analysis","score":0.007799999788403511,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/algorithmic-fairness","display_name":"algorithmic fairness","score":0.8920000195503235},{"id":"https://openalex.org/keywords/protected-characteristics","display_name":"protected characteristics","score":0.5709999799728394},{"id":"https://openalex.org/keywords/facial-images","display_name":"face images","score":0.42500001192092896},{"id":"https://openalex.org/keywords/gender-bias","display_name":"gender bias","score":0.5870000123977661},{"id":"https://openalex.org/keywords/equality-of-opportunity","display_name":"equal opportunities","score":0.9200000166893005},{"id":"https://openalex.org/keywords/residual-statistics","display_name":"residual statistics","score":0.5820000171661377},{"id":"https://openalex.org/keywords/celeba","display_name":"CelebA","score":0.7760000228881836},{"id":"https://openalex.org/keywords/adult-income-dataset","display_name":"Adult Income dataset","score":0.8309999704360962},{"id":"https://openalex.org/keywords/interpretable-representations","display_name":"interpretable representations","score":0.49799999594688416}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.746567964553833},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7294868230819702},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6314138770103455},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5801931619644165},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.553429126739502},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5533514022827148},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5430114269256592},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.43597400188446045},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4229009747505188},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.37392517924308777},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13000717759132385},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","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},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"pmh:oai:arXiv.org:1810.06755","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1810.06755","pdf_url":"https://arxiv.org/pdf/1810.06755","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"mag:2952676795","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1810.06755.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:figshare.com:article/23467319","doi":null,"is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/Discovering_fair_representations_in_the_data_domain/23467319","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference contribution"},{"id":"doi:10.48550/arxiv.1810.06755","doi":"https://doi.org/10.48550/arxiv.1810.06755","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1810.06755","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1810.06755","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1810.06755","pdf_url":"https://arxiv.org/pdf/1810.06755","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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"},"sustainable_development_goals":[{"score":0.9629999995231628,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"study_designs":[],"awards":[{"id":"https://openalex.org/G3190329379","display_name":"EthicalML: Injecting Ethical and Legal Constraints into Machine Learning Models","funder_award_id":"EP/P03442X/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"id":"https://openalex.org/F4320320283","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10"},{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2952676795.pdf","grobid_xml":"https://content.openalex.org/works/W2952676795.grobid-xml"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W1638081485","https://openalex.org/W1686810756","https://openalex.org/W1731081199","https://openalex.org/W1834627138","https://openalex.org/W1905153633","https://openalex.org/W1915485278","https://openalex.org/W1924619199","https://openalex.org/W1993766142","https://openalex.org/W2116984840","https://openalex.org/W2125389028","https://openalex.org/W2127658298","https://openalex.org/W2133284182","https://openalex.org/W2142187478","https://openalex.org/W2162670686","https://openalex.org/W2171408260","https://openalex.org/W2173905264","https://openalex.org/W2194775991","https://openalex.org/W2212660284","https://openalex.org/W2247194987","https://openalex.org/W2295130376","https://openalex.org/W2331128040","https://openalex.org/W2404287312","https://openalex.org/W2530395818","https://openalex.org/W2543774860","https://openalex.org/W2552465644","https://openalex.org/W2572730214","https://openalex.org/W2594475271","https://openalex.org/W2725155646","https://openalex.org/W2767437859","https://openalex.org/W2768626898","https://openalex.org/W2784397426","https://openalex.org/W2786122898","https://openalex.org/W2788416960","https://openalex.org/W2914454064","https://openalex.org/W2962750142","https://openalex.org/W2962793481","https://openalex.org/W2962922829","https://openalex.org/W2963174898","https://openalex.org/W2963327716","https://openalex.org/W2964121744","https://openalex.org/W2964316623","https://openalex.org/W3037302481","https://openalex.org/W3122175177","https://openalex.org/W4289258088","https://openalex.org/W4394656203"],"related_works":["https://openalex.org/W3048634599","https://openalex.org/W4400024632","https://openalex.org/W6981824882","https://openalex.org/W2904539038","https://openalex.org/W3118938787","https://openalex.org/W2982134689","https://openalex.org/W3127603246","https://openalex.org/W3183009143","https://openalex.org/W3190916233","https://openalex.org/W4383649507"],"abstract_inverted_index":{"Interpretability":[0],"and":[1,8,54,156,161,191,253,273],"fairness":[2,41,101,268],"are":[3,55],"critical":[4],"in":[5,12],"computer":[6],"vision":[7],"machine":[9],"learning":[10,44,87],"applications,":[11],"particular":[13],"when":[14,204],"dealing":[15],"with":[16,174,221,237],"human":[17],"outcomes,":[18],"e.g.":[19],"inviting":[20,23],"or":[21,113],"not":[22,133],"for":[24,211],"a":[25,88,95,100,141],"job":[26],"interview":[27],"based":[28],"on":[29,195],"application":[30],"materials":[31],"that":[32,47],"may":[33],"include":[34],"photographs.":[35],"One":[36],"promising":[37],"direction":[38],"to":[39,58,79,94,167,263],"achieve":[40],"is":[42,103,132],"by":[43,145,187],"data":[45,108,116,128,155],"representations":[46,208],"remove":[48],"the":[49,72,107,134,151,162,168,178,189,196,217,222,232,251,265],"semantics":[50],"of":[51,74,148,171,185,209,216,245,267],"protected":[52,163,179,238,271],"characteristics,":[53,272],"therefore":[56],"able":[57],"mitigate":[59],"unfair":[60],"outcomes.":[61],"All":[62],"available":[63],"models":[64],"however":[65],"learn":[66,140],"latent":[67],"embeddings":[68],"which":[69],"comes":[70],"at":[71,201],"cost":[73],"being":[75,104],"uninterpretable.":[76],"We":[77],"propose":[78],"cast":[80],"this":[81,131],"problem":[82],"as":[83,177],"data-to-data":[84],"translation,":[85],"i.e.":[86],"mapping":[89,144],"from":[90],"an":[91],"input":[92,154],"domain":[93,109],"fair":[96,126],"target":[97,127],"domain,":[98],"where":[99],"definition":[102],"enforced.":[105],"Here":[106],"can":[110],"be":[111,121],"images,":[112],"any":[114],"tabular":[115],"representation.":[117],"This":[118],"task":[119],"would":[120],"straightforward":[122],"if":[123],"we":[124,139,199],"had":[125],"available,":[129],"but":[130],"case.":[135],"To":[136],"overcome":[137],"this,":[138],"highly":[142],"unconstrained":[143],"exploiting":[146],"statistics":[147],"residuals":[149],"-":[150,160],"difference":[152],"between":[153],"its":[157],"translated":[158],"version":[159],"characteristics.":[164],"When":[165],"applied":[166],"CelebA":[169],"dataset":[170,198],"face":[172,214],"images":[173,210,215],"gender":[175,224,239],"attribute":[176,207],"characteristic,":[180],"our":[181,226,241],"model":[182,242],"enforces":[183],"equality":[184,244],"opportunity":[186,246],"adjusting":[188],"eyes":[190],"lips":[192],"regions.":[193,230],"Intriguingly,":[194],"same":[197,223],"arrive":[200],"similar":[202],"conclusions":[203],"using":[205],"semantic":[206],"translation.":[212],"On":[213],"recent":[218],"DiF":[219],"dataset,":[220,235],"attribute,":[225,240],"method":[227],"adjusts":[228],"nose":[229],"In":[231],"Adult":[233],"income":[234],"also":[236],"achieves":[243],"by,":[247],"among":[248],"others,":[249],"obfuscating":[250],"wife":[252],"husband":[254],"relationship.":[255],"Analyzing":[256],"those":[257],"systematic":[258],"changes":[259],"will":[260],"allow":[261],"us":[262],"scrutinize":[264],"interplay":[266],"criterion,":[269],"chosen":[270],"prediction":[274],"performance.":[275]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":6}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2019-06-27T00:00:00"}