{"id":"https://openalex.org/W4391094470","doi":"https://doi.org/10.1109/bigdata59044.2023.10386181","title":"Mitigating Representation Bias in Data Transformations: A Constraint-based Optimization Approach","display_name":"Mitigating Representation Bias in Data Transformations: A Constraint-based Optimization Approach","publication_year":2023,"publication_date":"2023-12-15","ids":{"openalex":"https://openalex.org/W4391094470","doi":"https://doi.org/10.1109/bigdata59044.2023.10386181"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata59044.2023.10386181","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata59044.2023.10386181","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024551850","display_name":"Barbara Catania","orcid":"https://orcid.org/0000-0002-6443-169X"},"institutions":[{"id":"https://openalex.org/I83816512","display_name":"University of Genoa","ror":"https://ror.org/0107c5v14","country_code":"IT","type":"education","lineage":["https://openalex.org/I83816512"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Barbara Catania","raw_affiliation_strings":["University of Genoa,IBRIS,Italy","IBRIS, University of Genoa, Italy"],"affiliations":[{"raw_affiliation_string":"University of Genoa,IBRIS,Italy","institution_ids":["https://openalex.org/I83816512"]},{"raw_affiliation_string":"IBRIS, University of Genoa, Italy","institution_ids":["https://openalex.org/I83816512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020065998","display_name":"Giovanna Guerrini","orcid":"https://orcid.org/0000-0001-9125-9867"},"institutions":[{"id":"https://openalex.org/I83816512","display_name":"University of Genoa","ror":"https://ror.org/0107c5v14","country_code":"IT","type":"education","lineage":["https://openalex.org/I83816512"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Giovanna Guerrini","raw_affiliation_strings":["University of Genoa,IBRIS,Italy","IBRIS, University of Genoa, Italy"],"affiliations":[{"raw_affiliation_string":"University of Genoa,IBRIS,Italy","institution_ids":["https://openalex.org/I83816512"]},{"raw_affiliation_string":"IBRIS, University of Genoa, Italy","institution_ids":["https://openalex.org/I83816512"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5093760402","display_name":"Ziad Janpih","orcid":null},"institutions":[{"id":"https://openalex.org/I83816512","display_name":"University of Genoa","ror":"https://ror.org/0107c5v14","country_code":"IT","type":"education","lineage":["https://openalex.org/I83816512"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Ziad Janpih","raw_affiliation_strings":["University of Genoa,IBRIS,Italy","IBRIS, University of Genoa, Italy"],"affiliations":[{"raw_affiliation_string":"University of Genoa,IBRIS,Italy","institution_ids":["https://openalex.org/I83816512"]},{"raw_affiliation_string":"IBRIS, University of Genoa, Italy","institution_ids":["https://openalex.org/I83816512"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5024551850"],"corresponding_institution_ids":["https://openalex.org/I83816512"],"apc_list":null,"apc_paid":null,"fwci":0.2785,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.66883421,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"42","issue":null,"first_page":"4127","last_page":"4136"},"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":0.9977999925613403,"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":0.9977999925613403,"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":0.9966999888420105,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9944000244140625,"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/computer-science","display_name":"Computer science","score":0.7593516111373901},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.7090427279472351},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6198958158493042},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6051664352416992},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5887803435325623},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5409425497055054},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5318564772605896},{"id":"https://openalex.org/keywords/external-data-representation","display_name":"External Data Representation","score":0.5224103927612305},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.5218371748924255},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5073854327201843},{"id":"https://openalex.org/keywords/data-transformation","display_name":"Data transformation","score":0.4395481050014496},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.43859779834747314},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.4379129409790039},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41118568181991577},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09939128160476685}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7593516111373901},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.7090427279472351},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6198958158493042},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6051664352416992},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5887803435325623},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5409425497055054},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5318564772605896},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.5224103927612305},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.5218371748924255},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5073854327201843},{"id":"https://openalex.org/C150670458","wikidata":"https://www.wikidata.org/wiki/Q4272815","display_name":"Data transformation","level":3,"score":0.4395481050014496},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.43859779834747314},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.4379129409790039},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41118568181991577},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09939128160476685},{"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C135572916","wikidata":"https://www.wikidata.org/wiki/Q193351","display_name":"Data warehouse","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata59044.2023.10386181","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata59044.2023.10386181","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},{"id":"pmh:oai:iris.unige.it:11567/1173916","is_oa":false,"landing_page_url":"https://hdl.handle.net/11567/1173916","pdf_url":null,"source":{"id":"https://openalex.org/S4377196291","display_name":"CINECA IRIS Institutial Research Information System (University of Genoa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I83816512","host_organization_name":"University of Genoa","host_organization_lineage":["https://openalex.org/I83816512"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2666919929","https://openalex.org/W2807817912","https://openalex.org/W2809878087","https://openalex.org/W2882995207","https://openalex.org/W2896331720","https://openalex.org/W2966337615","https://openalex.org/W3031292160","https://openalex.org/W3041268394","https://openalex.org/W3082499364","https://openalex.org/W3086663505","https://openalex.org/W3107629419","https://openalex.org/W3108436891","https://openalex.org/W3166077024","https://openalex.org/W3174324482","https://openalex.org/W3181414820","https://openalex.org/W3214911045","https://openalex.org/W4284890692","https://openalex.org/W4288259571","https://openalex.org/W4289534040","https://openalex.org/W4293431124","https://openalex.org/W4298184563","https://openalex.org/W4320024246","https://openalex.org/W6766643478","https://openalex.org/W6767441432","https://openalex.org/W6776188237","https://openalex.org/W6786498897","https://openalex.org/W6786944003","https://openalex.org/W6792591181","https://openalex.org/W6840303649","https://openalex.org/W6844325931"],"related_works":["https://openalex.org/W1492261612","https://openalex.org/W2374519217","https://openalex.org/W2012941781","https://openalex.org/W2156949561","https://openalex.org/W2393271921","https://openalex.org/W2089133691","https://openalex.org/W2041526955","https://openalex.org/W2755827507","https://openalex.org/W2048936104","https://openalex.org/W2392646943"],"abstract_inverted_index":{"The":[0,197],"development":[1,46],"of":[2,7,16,24,30,47,59,76,79,168,171,181],"discrimination-aware":[3],"solutions":[4],"is":[5],"one":[6],"the":[8,14,37,45,54,57,63,74,82,89,106,119,159,179,182,190,204],"main":[9,28],"current":[10],"research":[11],"directions":[12],"in":[13,56,81,144,166,189,211],"design":[15],"automated":[17],"decision":[18],"systems":[19],"that":[20,49,203],"make":[21],"deep":[22],"use":[23],"people-related":[25],"data.":[26],"Two":[27],"groups":[29,78],"techniques":[31],"have":[32],"been":[33],"designed":[34],"so":[35],"far:":[36],"first":[38],"focuses":[39],"on":[40,132],"fair":[41],"machine":[42],"learning,":[43],"i.e.,":[44],"algorithms":[48],"can":[50,100,113,162,207],"detect":[51],"and":[52,69,104,135,175,214],"correct":[53],"bias":[55,71,99,134,143,164,210],"context":[58,192],"a":[60,150,194],"learning":[61],"process;":[62],"second":[64],"targets":[65],"approaches":[66],"for":[67,140],"detecting":[68],"mitigating":[70,141],"due":[72],"to":[73,93],"under-representation":[75],"specific":[77,151],"people":[80],"used":[83],"dataset":[84],"(representation":[85],"bias),":[86],"independently":[87],"from":[88,102,156],"further":[90],"analytical":[91,125],"tasks":[92],"be":[94,114],"executed.":[95],"In":[96,127],"both":[97],"cases,":[98],"originate":[101],"how":[103],"where":[105],"data":[107,120,148,152],"was":[108],"originally":[109],"collected":[110],"or":[111],"it":[112],"introduced,":[115],"sometimes":[116],"amplified,":[117],"during":[118],"preparation":[121,153],"steps":[122],"preceding":[123],"any":[124],"task.":[126],"this":[128],"paper,":[129],"we":[130,136,185],"focus":[131],"representation":[133,142,209],"present":[137],"an":[138,212],"approach":[139],"datasets":[145],"generated":[146],"through":[147],"transformation,":[149],"phase.":[154],"Differently":[155],"similar":[157],"proposals,":[158],"proposed":[160,183,205],"technique":[161,206],"mitigate":[163,208],"defined":[165],"terms":[167],"multiple":[169],"types":[170],"constraints,":[172],"including":[173],"coverage":[174],"fairness.":[176],"To":[177],"show":[178,202],"applicability":[180],"approach,":[184],"consider":[186],"dropout":[187],"prediction":[188],"educational":[191],"as":[193],"case":[195],"study.":[196],"preliminary":[198],"reported":[199],"experimental":[200],"results":[201],"effective":[213],"efficient":[215],"way.":[216]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}