{"id":"https://openalex.org/W7160388936","doi":"https://doi.org/10.1109/saner-c67878.2026.00024","title":"Evaluation of Data Quality Disparity and Implications for Fair Machine Learning","display_name":"Evaluation of Data Quality Disparity and Implications for Fair Machine Learning","publication_year":2026,"publication_date":"2026-03-17","ids":{"openalex":"https://openalex.org/W7160388936","doi":"https://doi.org/10.1109/saner-c67878.2026.00024"},"language":null,"primary_location":{"id":"doi:10.1109/saner-c67878.2026.00024","is_oa":false,"landing_page_url":"https://doi.org/10.1109/saner-c67878.2026.00024","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE International Conference on Software Analysis, Evolution and Reengineering - Companion (SANER-C)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5013833501","display_name":"Mohit Sharma","orcid":"https://orcid.org/0000-0002-6045-9716"},"institutions":[{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Mohit Sharma","raw_affiliation_strings":["IIT Delhi"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IIT Delhi","institution_ids":["https://openalex.org/I68891433"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135461330","display_name":"Pratik Mishra","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pratik Mishra","raw_affiliation_strings":["IBM Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039554087","display_name":"Sandeep Hans","orcid":"https://orcid.org/0000-0003-4986-0688"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sandeep Hans","raw_affiliation_strings":["IBM Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040381142","display_name":"Abhijnan Chakraborty","orcid":"https://orcid.org/0000-0003-0908-1639"},"institutions":[{"id":"https://openalex.org/I145894827","display_name":"Indian Institute of Technology Kharagpur","ror":"https://ror.org/03w5sq511","country_code":"IN","type":"education","lineage":["https://openalex.org/I145894827"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Abhijnan Chakraborty","raw_affiliation_strings":["IIT Kharagpur"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IIT Kharagpur","institution_ids":["https://openalex.org/I145894827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056759720","display_name":"Vijay Arya","orcid":"https://orcid.org/0000-0001-8892-6761"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vijay Arya","raw_affiliation_strings":["IBM Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research","institution_ids":[]}]}],"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":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"141","last_page":"148"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.5616999864578247,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.5616999864578247,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.24150000512599945,"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/T14347","display_name":"Big Data and Digital Economy","score":0.020600000396370888,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/quality","display_name":"Quality (philosophy)","score":0.4823000133037567},{"id":"https://openalex.org/keywords/data-quality","display_name":"Data quality","score":0.42739999294281006},{"id":"https://openalex.org/keywords/data-collection","display_name":"Data collection","score":0.295199990272522},{"id":"https://openalex.org/keywords/real-world-data","display_name":"Real world data","score":0.2793000042438507},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.2572999894618988}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5720999836921692},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5667999982833862},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4921000003814697},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4823000133037567},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.42739999294281006},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.295199990272522},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2897999882698059},{"id":"https://openalex.org/C3020493868","wikidata":"https://www.wikidata.org/wiki/Q55631277","display_name":"Real world data","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.24369999766349792}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/saner-c67878.2026.00024","is_oa":false,"landing_page_url":"https://doi.org/10.1109/saner-c67878.2026.00024","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE International Conference on Software Analysis, Evolution and Reengineering - Companion (SANER-C)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2067260180","https://openalex.org/W2116984840","https://openalex.org/W2146950091","https://openalex.org/W2592232824","https://openalex.org/W2754213847","https://openalex.org/W2800499454","https://openalex.org/W2807841289","https://openalex.org/W2974735009","https://openalex.org/W3092541244","https://openalex.org/W3101820912","https://openalex.org/W3120485916","https://openalex.org/W3137010024","https://openalex.org/W3200170939","https://openalex.org/W4220699706","https://openalex.org/W4241162727","https://openalex.org/W4289258088","https://openalex.org/W4382462760","https://openalex.org/W4390229867","https://openalex.org/W4393078930","https://openalex.org/W4399363450","https://openalex.org/W4401540052","https://openalex.org/W4403923277","https://openalex.org/W4404974879"],"related_works":[],"abstract_inverted_index":{"The":[0],"performance":[1,103],"of":[2,12],"machine":[3],"learning":[4,148],"(ML)":[5],"models":[6,85],"heavily":[7],"depends":[8],"on":[9,86],"the":[10,13,129,139],"quality":[11,25,133],"data":[14,24,76,88,132,144],"they":[15],"are":[16,151],"trained":[17],"on.":[18],"While":[19],"prior":[20],"work":[21,127],"often":[22],"treats":[23],"as":[26,52],"uniform":[27],"across":[28,36,104],"a":[29,41,47],"dataset,":[30],"we":[31,49],"investigate":[32],"whether":[33],"it":[34],"varies":[35],"different":[37],"population":[38],"subgroups":[39],"within":[40],"dataset":[42],"and":[43,112,122,134,137,147],"examine":[44],"its":[45,114],"implications,":[46],"phenomenon":[48],"refer":[50],"to":[51,78,100,109],"Data":[53],"Quality":[54],"Disparity":[55],"(DQD).":[56],"Our":[57,126],"analysis":[58],"reveals":[59],"that":[60,150],"many":[61],"real-world":[62],"datasets":[63],"inherently":[64],"exhibit":[65],"DQD,":[66],"with":[67,74],"underrepresented":[68],"or":[69,89],"marginalized":[70],"groups":[71],"frequently":[72],"associated":[73],"lower-quality":[75],"compared":[77],"more":[79],"privileged":[80],"subgroups.":[81],"Consequently,":[82],"training":[83],"ML":[84],"raw":[87],"applying":[90],"standard":[91],"preprocessing":[92],"techniques":[93],"without":[94],"accounting":[95],"for":[96,116,141],"DQD":[97,111],"can":[98],"lead":[99],"uneven":[101],"prediction":[102],"demographics.":[105],"We":[106],"introduce":[107],"metrics":[108],"quantify":[110],"assess":[113],"potential":[115],"discrimination,":[117],"using":[118],"both":[119],"structured":[120],"(tabular)":[121],"unstructured":[123],"(image)":[124],"datasets.":[125],"explores":[128],"connections":[130],"between":[131],"algorithmic":[135],"fairness":[136],"underscores":[138],"need":[140],"developing":[142],"fair":[143],"processing":[145],"pipelines":[146],"algorithms":[149],"DQD-aware.":[152]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-05-07T00:00:00"}