{"id":"https://openalex.org/W3154366205","doi":"https://doi.org/10.48550/arxiv.2104.04353","title":"Implementing Fair Regression In The Real World","display_name":"Implementing Fair Regression In The Real World","publication_year":2021,"publication_date":"2021-04-09","ids":{"openalex":"https://openalex.org/W3154366205","doi":"https://doi.org/10.48550/arxiv.2104.04353","mag":"3154366205"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2104.04353","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2104.04353","pdf_url":"https://arxiv.org/pdf/2104.04353","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2104.04353","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039251226","display_name":"Boris Ruf","orcid":"https://orcid.org/0000-0001-9024-9499","observed_orcids":["https://orcid.org/0000-0001-9024-9499"]},"institutions":[{"id":"https://openalex.org/I4210100579","display_name":"AXA (France)","ror":"https://ror.org/013z4qk72","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210100579"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Ruf, Boris","raw_affiliation_strings":["AXA , AI Research Paris , France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AXA , AI Research Paris , France","institution_ids":["https://openalex.org/I4210100579"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065404842","display_name":"Marcin Detyniecki","orcid":"https://orcid.org/0000-0001-5669-4871","observed_orcids":["https://orcid.org/0000-0001-5669-4871"]},"institutions":[{"id":"https://openalex.org/I4210100579","display_name":"AXA (France)","ror":"https://ror.org/013z4qk72","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210100579"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Detyniecki, Marcin","raw_affiliation_strings":["AXA , AI Research Paris , France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AXA , AI Research Paris , France","institution_ids":["https://openalex.org/I4210100579"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210100579"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.989300012588501,"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"}},"topics":[{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.989300012588501,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.001500000013038516,"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":0.0008999999845400453,"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/algorithmic-fairness","display_name":"algorithmic fairness","score":0.9290000200271606},{"id":"https://openalex.org/keywords/individual-fairness","display_name":"individual fairness","score":0.6779999732971191},{"id":"https://openalex.org/keywords/post-processing","display_name":"post-processing","score":0.7639999985694885},{"id":"https://openalex.org/keywords/sensitive-subpopulations","display_name":"sensitive subpopulations","score":0.6380000114440918},{"id":"https://openalex.org/keywords/bias-mitigation","display_name":"bias mitigation","score":0.3659999966621399},{"id":"https://openalex.org/keywords/continuous-prediction","display_name":"continuous prediction","score":0.3930000066757202}],"concepts":[{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.7324935793876648},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6753944158554077},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.5395689606666565},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5251129269599915},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.49573004245758057},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.430469274520874},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4169890880584717},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38282305002212524},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3565589189529419},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3555740416049957},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33200621604919434},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2989071011543274},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17821010947227478},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2104.04353","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2104.04353","pdf_url":"https://arxiv.org/pdf/2104.04353","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2104.04353","doi":"https://doi.org/10.48550/arxiv.2104.04353","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2104.04353","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2104.04353","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2104.04353","pdf_url":"https://arxiv.org/pdf/2104.04353","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.7578999996185303,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3154366205.pdf","grobid_xml":"https://content.openalex.org/works/W3154366205.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2946280906","https://openalex.org/W4396814171","https://openalex.org/W3202368036","https://openalex.org/W7056479672","https://openalex.org/W3005657488","https://openalex.org/W3019370719","https://openalex.org/W2622808887","https://openalex.org/W4225552009","https://openalex.org/W7141044561","https://openalex.org/W4415311304"],"abstract_inverted_index":{"Most":[0],"fair":[1,28,47,56,82],"regression":[2,29,57,83],"algorithms":[3,54,58,74],"mitigate":[4],"bias":[5],"towards":[6],"sensitive":[7],"sub":[8],"populations":[9],"and":[10],"therefore":[11],"improve":[12,76],"fairness":[13],"at":[14],"group":[15],"level.":[16],"In":[17],"this":[18],"paper,":[19],"we":[20,35,68],"investigate":[21],"the":[22,31,37,60,77,80],"impact":[23],"of":[24,27,39,72,79],"such":[25],"implementation":[26],"on":[30,65],"individual.":[32],"More":[33],"precisely,":[34],"assess":[36],"evolution":[38],"continuous":[40],"predictions":[41],"from":[42,52],"an":[43],"unconstrained":[44],"to":[45,75],"a":[46,70],"algorithm":[48],"by":[49],"comparing":[50],"results":[51],"baseline":[53],"with":[55],"for":[59],"same":[61],"data":[62],"points.":[63],"Based":[64],"our":[66],"findings,":[67],"propose":[69],"set":[71],"post-processing":[73],"utility":[78],"existing":[81],"approaches.":[84]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2021-04-26T00:00:00"}