{"id":"https://openalex.org/W7148065617","doi":"https://doi.org/10.48550/arxiv.2603.29270","title":"Unbiased Model Prediction Without Using Protected Attribute Information","display_name":"Unbiased Model Prediction Without Using Protected Attribute Information","publication_year":2026,"publication_date":"2026-03-31","ids":{"openalex":"https://openalex.org/W7148065617","doi":"https://doi.org/10.48550/arxiv.2603.29270"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.29270","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29270","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.29270","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009237168","display_name":"Puspita Majumdar","orcid":"https://orcid.org/0000-0002-4031-785X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Majumdar, Puspita","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132619908","display_name":"Surbhi Mittal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mittal, Surbhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Chhabra, Saheb","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chhabra, Saheb","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Vatsa, Mayank","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vatsa, Mayank","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Singh, Richa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Singh, Richa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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/T11448","display_name":"Face recognition and analysis","score":0.7894999980926514,"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"}},"topics":[{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.7894999980926514,"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"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.044599998742341995,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.03229999914765358,"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/debiasing","display_name":"Debiasing","score":0.9014000296592712},{"id":"https://openalex.org/keywords/information-loss","display_name":"Information loss","score":0.5501999855041504},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5249000191688538},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.3314000070095062},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2939000129699707},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.28139999508857727}],"concepts":[{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.9014000296592712},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6439999938011169},{"id":"https://openalex.org/C2988416141","wikidata":"https://www.wikidata.org/wiki/Q6031139","display_name":"Information loss","level":2,"score":0.5501999855041504},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5266000032424927},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5249000191688538},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5198000073432922},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43470001220703125},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3968000113964081},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.3314000070095062},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2939000129699707},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C3020402766","wikidata":"https://www.wikidata.org/wiki/Q104376712","display_name":"Prior information","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C2983427547","wikidata":"https://www.wikidata.org/wiki/Q93200","display_name":"Gender bias","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C2779714256","wikidata":"https://www.wikidata.org/wiki/Q25305062","display_name":"Multiple Models","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26019999384880066}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.29270","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29270","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.29270","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29270","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/5","display_name":"Gender equality","score":0.736184298992157}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,84],"problem":[1],"of":[2,31,37],"bias":[3,46,74,102,149],"persists":[4],"in":[5,53,148],"the":[6,29,41,80,89,94,99,125,135],"deep":[7,32],"learning":[8],"community":[9],"as":[10,67],"models":[11],"continue":[12],"to":[13,27,97,123],"provide":[14],"disparate":[15],"performance":[16],"across":[17,150],"different":[18,106,151],"demographic":[19],"subgroups.":[20],"Therefore,":[21],"several":[22],"algorithms":[23,39],"have":[24,61,120],"been":[25,121],"proposed":[26,62,85,122],"improve":[28],"fairness":[30,128],"models.":[33],"However,":[34],"a":[35,63,145],"majority":[36],"these":[38],"utilize":[40],"protected":[42,81],"attribute":[43,82,142],"information":[44,91],"for":[45,73,101,127,140],"mitigation,":[47,75],"which":[48],"severely":[49],"limits":[50],"their":[51],"application":[52],"real-world":[54],"scenarios.":[55],"To":[56],"address":[57],"this":[58],"concern,":[59],"we":[60],"novel":[64],"algorithm,":[65],"termed":[66],"\\textbf{Non-Protected":[68],"Attribute-based":[69],"Debiasing":[70],"(NPAD)}":[71],"algorithm":[72,87],"that":[76],"does":[77],"not":[78],"require":[79],"information.":[83],"NPAD":[86],"utilizes":[88],"auxiliary":[90],"provided":[92],"by":[93],"non-protected":[95],"attributes":[96],"optimize":[98,124],"model":[100,126],"mitigation.":[103],"Further,":[104],"two":[105],"loss":[107],"functions,":[108],"\\textbf{Debiasing":[109],"via":[110],"Attribute":[111],"Cluster":[112],"Loss":[113,118],"(DACL)}":[114],"and":[115,137,144,153],"\\textbf{Filter":[116],"Redundancy":[117],"(FRL)}":[119],"goals.":[129],"Multiple":[130],"experiments":[131],"are":[132],"performed":[133],"on":[134],"LFWA":[136],"CelebA":[138],"datasets":[139],"facial":[141],"prediction,":[143],"significant":[146],"reduction":[147],"gender":[152],"age":[154],"subgroups":[155],"is":[156],"observed.":[157]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-02T00:00:00"}