{"id":"https://openalex.org/W4415428513","doi":"https://doi.org/10.3233/faia251105","title":"Achieving Intersectional Algorithmic Fairness by Constructing a Maximal Correlation Latent Space","display_name":"Achieving Intersectional Algorithmic Fairness by Constructing a Maximal Correlation Latent Space","publication_year":2025,"publication_date":"2025-10-21","ids":{"openalex":"https://openalex.org/W4415428513","doi":"https://doi.org/10.3233/faia251105"},"language":"en","primary_location":{"id":"doi:10.3233/faia251105","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251105","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.3233/faia251105","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103011136","display_name":"Luca Giuliani","orcid":"https://orcid.org/0000-0002-9120-6949"},"institutions":[{"id":"https://openalex.org/I9360294","display_name":"University of Bologna","ror":"https://ror.org/01111rn36","country_code":"IT","type":"education","lineage":["https://openalex.org/I9360294"]},{"id":"https://openalex.org/I4210097606","display_name":"GNA University","ror":"https://ror.org/00cy7e479","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210097606"]}],"countries":["IN","IT"],"is_corresponding":true,"raw_author_name":"Luca Giuliani","raw_affiliation_strings":["University of Bologna, Department of Computer Science and Engineering (DISI)"],"affiliations":[{"raw_affiliation_string":"University of Bologna, Department of Computer Science and Engineering (DISI)","institution_ids":["https://openalex.org/I9360294","https://openalex.org/I4210097606"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016899356","display_name":"Michele Lombardi","orcid":"https://orcid.org/0000-0002-8613-053X"},"institutions":[{"id":"https://openalex.org/I9360294","display_name":"University of Bologna","ror":"https://ror.org/01111rn36","country_code":"IT","type":"education","lineage":["https://openalex.org/I9360294"]},{"id":"https://openalex.org/I4210097606","display_name":"GNA University","ror":"https://ror.org/00cy7e479","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210097606"]}],"countries":["IN","IT"],"is_corresponding":false,"raw_author_name":"Michele Lombardi","raw_affiliation_strings":["University of Bologna, Department of Computer Science and Engineering (DISI)"],"affiliations":[{"raw_affiliation_string":"University of Bologna, Department of Computer Science and Engineering (DISI)","institution_ids":["https://openalex.org/I9360294","https://openalex.org/I4210097606"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5103011136"],"corresponding_institution_ids":["https://openalex.org/I4210097606","https://openalex.org/I9360294"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.53532153,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":0.9663000106811523,"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":0.9663000106811523,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.6503000259399414},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.5482000112533569},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.47360000014305115},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4595000147819519},{"id":"https://openalex.org/keywords/extension","display_name":"Extension (predicate logic)","score":0.4471000134944916},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.44200000166893005},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.43950000405311584},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.32820001244544983}],"concepts":[{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.6503000259399414},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5975000262260437},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.5482000112533569},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47360000014305115},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4595000147819519},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.4471000134944916},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.44200000166893005},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.43950000405311584},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.37279999256134033},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3707999885082245},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3580999970436096},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3343999981880188},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32820001244544983},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.32820001244544983},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3273000121116638},{"id":"https://openalex.org/C2777151079","wikidata":"https://www.wikidata.org/wiki/Q141160","display_name":"Parity (physics)","level":2,"score":0.32019999623298645},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3127000033855438},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C70727504","wikidata":"https://www.wikidata.org/wiki/Q1806878","display_name":"Latent class model","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C2988382989","wikidata":"https://www.wikidata.org/wiki/Q370685","display_name":"Data space","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C2780001913","wikidata":"https://www.wikidata.org/wiki/Q1516555","display_name":"Intersectionality","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.257099986076355},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3233/faia251105","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251105","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},{"id":"pmh:oai:cris.unibo.it:11585/1056250","is_oa":false,"landing_page_url":"https://hdl.handle.net/11585/1056250","pdf_url":null,"source":{"id":"https://openalex.org/S4306402579","display_name":"Archivio istituzionale della ricerca (Alma Mater Studiorum Universit\u00e0 di Bologna)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210117483","host_organization_name":"Istituto di Ematologia di Bologna","host_organization_lineage":["https://openalex.org/I4210117483"],"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":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"doi:10.3233/faia251105","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251105","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"developments":[1],"in":[2],"algorithmic":[3],"fairness":[4,25],"started":[5],"to":[6,43,97,106],"investigate":[7],"the":[8,32,57],"interaction":[9],"between":[10],"multiple":[11,45],"sensitive":[12],"information":[13],"through":[14],"an":[15],"intersectional":[16,24,99],"perspective.":[17],"We":[18],"introduce":[19],"a":[20,28,40,49,85],"new":[21],"definition":[22],"of":[23,31],"based":[26],"on":[27,73],"multivariate":[29],"extension":[30],"Generalized":[33],"Disparate":[34],"Impact":[35],"(GeDI).":[36],"Our":[37],"approach":[38],"leverages":[39],"neural":[41,89],"network":[42,90],"transform":[44],"protected":[46],"groups":[47],"into":[48],"univariate":[50],"latent":[51],"space":[52],"that":[53,77],"maximizes":[54],"correlation":[55],"with":[56,67],"target,":[58],"effectively":[59,82],"capturing":[60],"unfairness":[61],"across":[62],"all":[63],"potential":[64],"subgroups":[65],"even":[66],"limited":[68],"data":[69],"samples.":[70],"Empirical":[71],"evaluations":[72],"several":[74],"benchmarks":[75],"demonstrate":[76],"our":[78],"method":[79],"can":[80],"be":[81],"used":[83],"as":[84],"loss":[86],"regularizer":[87],"during":[88],"training,":[91],"offering":[92],"stronger":[93],"performance":[94],"guarantees":[95],"compared":[96],"existing":[98],"statistical":[100],"parity":[101],"definitions":[102],"while":[103],"also":[104],"allowing":[105],"manage":[107],"continuous":[108],"inputs":[109],"and":[110],"targets.":[111]},"counts_by_year":[],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-24T00:00:00"}