{"id":"https://openalex.org/W6929274886","doi":"https://doi.org/10.48550/arxiv.2506.06243","title":"Fairmetrics: An R package for group fairness evaluation","display_name":"Fairmetrics: An R package for group fairness evaluation","publication_year":2025,"publication_date":"2025-06-06","ids":{"openalex":"https://openalex.org/W6929274886","doi":"https://doi.org/10.48550/arxiv.2506.06243"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2506.06243","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2506.06243","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.2506.06243","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Smith, Benjamin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Smith, Benjamin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Gao, Jianhui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Jianhui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Gronsbell, Jessica","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gronsbell, Jessica","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":true,"primary_topic":{"id":"https://openalex.org/T10885","display_name":"Gene expression and cancer classification","score":0.3003999888896942,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10885","display_name":"Gene expression and cancer classification","score":0.3003999888896942,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.042100001126527786,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.037700001150369644,"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/set","display_name":"Set (abstract data type)","score":0.6202999949455261},{"id":"https://openalex.org/keywords/fairness-measure","display_name":"Fairness measure","score":0.4927999973297119},{"id":"https://openalex.org/keywords/independence","display_name":"Independence (probability theory)","score":0.4925999939441681},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.49239999055862427},{"id":"https://openalex.org/keywords/group","display_name":"Group (periodic table)","score":0.486299991607666},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.46459999680519104},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4580000042915344}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6234999895095825},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6202999949455261},{"id":"https://openalex.org/C11867375","wikidata":"https://www.wikidata.org/wiki/Q5430671","display_name":"Fairness measure","level":4,"score":0.4927999973297119},{"id":"https://openalex.org/C35651441","wikidata":"https://www.wikidata.org/wiki/Q625303","display_name":"Independence (probability theory)","level":2,"score":0.4925999939441681},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.49239999055862427},{"id":"https://openalex.org/C2781311116","wikidata":"https://www.wikidata.org/wiki/Q83306","display_name":"Group (periodic table)","level":2,"score":0.486299991607666},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.46459999680519104},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4580000042915344},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44279998540878296},{"id":"https://openalex.org/C2984074130","wikidata":"https://www.wikidata.org/wiki/Q73539779","display_name":"R package","level":2,"score":0.3901999890804291},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3723999857902527},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3513000011444092},{"id":"https://openalex.org/C65660741","wikidata":"https://www.wikidata.org/wiki/Q3952743","display_name":"Score","level":2,"score":0.3287999927997589},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3206000030040741},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2856000065803528},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C3020493868","wikidata":"https://www.wikidata.org/wiki/Q55631277","display_name":"Real world data","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26030001044273376},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.25690001249313354}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2506.06243","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2506.06243","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.2506.06243","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2506.06243","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":[{"score":0.41025495529174805,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Fairness":[0],"is":[1,37,91],"a":[2,38,57,89,97,122],"growing":[3],"area":[4],"of":[5,41,99],"machine":[6],"learning":[7],"(ML)":[8],"that":[9,103],"focuses":[10],"on":[11,70],"ensuring":[12],"models":[13,47],"do":[14],"not":[15],"produce":[16],"systematically":[17],"biased":[18,46],"outcomes":[19],"for":[20,60,118,137],"specific":[21],"groups,":[22],"particularly":[23],"those":[24],"defined":[25],"by":[26],"protected":[27],"attributes":[28],"such":[29],"as":[30,45],"race,":[31],"gender,":[32],"or":[33,94],"age.":[34],"Evaluating":[35],"fairness":[36,65,85],"critical":[39],"aspect":[40],"ML":[42],"model":[43,90],"development,":[44],"can":[48,108],"perpetuate":[49],"structural":[50],"inequalities.":[51],"The":[52],"{fairmetrics}":[53,111],"R":[54],"package":[55],"offers":[56],"user-friendly":[58],"framework":[59],"rigorously":[61],"evaluating":[62],"numerous":[63],"group-based":[64],"criteria,":[66],"including":[67],"metrics":[68,120],"based":[69],"independence":[71],"(e.g.,":[72,76,81],"statistical":[73],"parity),":[74],"separation":[75],"equalized":[77],"odds),":[78],"and":[79,115,126],"sufficiency":[80],"predictive":[82],"parity).":[83],"Group-based":[84],"criteria":[86],"assess":[87],"whether":[88],"equally":[92],"accurate":[93],"well-calibrated":[95],"across":[96],"set":[98],"predefined":[100],"groups":[101],"so":[102],"appropriate":[104],"bias":[105],"mitigation":[106],"strategies":[107],"be":[109],"implemented.":[110],"provides":[112],"both":[113],"point":[114],"interval":[116],"estimates":[117],"multiple":[119],"through":[121],"convenient":[123],"wrapper":[124],"function":[125],"includes":[127],"an":[128],"example":[129],"dataset":[130],"derived":[131],"from":[132],"the":[133],"Medical":[134],"Information":[135],"Mart":[136],"Intensive":[138],"Care,":[139],"version":[140],"II":[141],"(MIMIC-II)":[142],"database":[143],"(Goldberger":[144],"et":[145],"al.,":[146],"2000;":[147],"Raffa,":[148],"2016).":[149]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}
