{"id":"https://openalex.org/W7137974214","doi":"https://doi.org/10.1609/aaai.v40i31.39793","title":"Decomposing Direct and Indirect Biases in Linear Models Under Demographic Parity Constraint","display_name":"Decomposing Direct and Indirect Biases in Linear Models Under Demographic Parity Constraint","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7137974214","doi":"https://doi.org/10.1609/aaai.v40i31.39793"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i31.39793","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i31.39793","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i31.39793","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120457292","display_name":"Bertille Tierny","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bertille Tierny","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129651129","display_name":"Arthur Charpentier","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arthur Charpentier","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5045398972","display_name":"Fran\u00e7ois Hu","orcid":"https://orcid.org/0009-0000-6093-6175"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Francois Hu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5120457292"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11764706,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"31","first_page":"25932","last_page":"25939"},"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":0.955299973487854,"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":0.955299973487854,"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":0.00930000003427267,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.007699999958276749,"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/linear-model","display_name":"Linear model","score":0.5859000086784363},{"id":"https://openalex.org/keywords/parity","display_name":"Parity (physics)","score":0.5467000007629395},{"id":"https://openalex.org/keywords/simplicity","display_name":"Simplicity","score":0.45260000228881836},{"id":"https://openalex.org/keywords/log-linear-model","display_name":"Log-linear model","score":0.4262000024318695},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.42329999804496765},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.4187999963760376},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.321399986743927}],"concepts":[{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.6000000238418579},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.5859000086784363},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5512999892234802},{"id":"https://openalex.org/C2777151079","wikidata":"https://www.wikidata.org/wiki/Q141160","display_name":"Parity (physics)","level":2,"score":0.5467000007629395},{"id":"https://openalex.org/C2776372474","wikidata":"https://www.wikidata.org/wiki/Q508291","display_name":"Simplicity","level":2,"score":0.45260000228881836},{"id":"https://openalex.org/C70519679","wikidata":"https://www.wikidata.org/wiki/Q6666755","display_name":"Log-linear model","level":3,"score":0.4262000024318695},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.42329999804496765},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.4187999963760376},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3652999997138977},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3506999909877777},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.321399986743927},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.3197000026702881},{"id":"https://openalex.org/C41587187","wikidata":"https://www.wikidata.org/wiki/Q1501882","display_name":"Generalized linear model","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2635999917984009},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.2549000084400177},{"id":"https://openalex.org/C2779714256","wikidata":"https://www.wikidata.org/wiki/Q25305062","display_name":"Multiple Models","level":2,"score":0.2540999948978424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i31.39793","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i31.39793","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i31.39793","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i31.39793","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.7977344989776611,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Linear":[0],"models":[1,45],"are":[2,22],"widely":[3],"used":[4],"in":[5],"high-stakes":[6],"decision-making":[7],"due":[8],"to":[9,84],"their":[10,24,63],"simplicity":[11],"and":[12,29,50,92,112,124,141,148,154,171],"interpretability.":[13],"Yet":[14],"when":[15],"fairness":[16,67,122,162],"constraints":[17],"such":[18],"as":[19],"demographic":[20,101],"parity":[21,102],"introduced,":[23],"effects":[25],"on":[26,31,43,48,78,151],"model":[27,83,105,139,146],"coefficients,":[28],"thus":[30],"how":[32,100,126],"predictive":[33],"bias":[34,88,127],"is":[35],"distributed":[36],"across":[37],"features,":[38],"remain":[39],"opaque.":[40],"Existing":[41],"approaches":[42],"linear":[44,82,178],"often":[46],"rely":[47],"strong":[49],"unrealistic":[51],"assumptions,":[52],"or":[53,130],"overlook":[54],"the":[55,59,86],"explicit":[56],"role":[57],"of":[58,80,109,121,177],"sensitive":[60,111],"attribute,":[61],"limiting":[62],"practical":[64,170],"utility":[65],"for":[66,145,174],"assessment.":[68],"We":[69],"propose":[70],"a":[71,117,169],"post-processing":[72],"framework":[73,136],"that":[74,158],"can":[75],"be":[76],"applied":[77],"top":[79],"any":[81],"decompose":[85],"resulting":[87],"into":[89],"direct":[90],"(sensitive-attribute)":[91],"indirect":[93],"(correlated-features)":[94],"components.":[95],"Our":[96,135],"method":[97,160],"analytically":[98],"characterizes":[99],"reshapes":[103],"each":[104],"coefficient,":[106],"including":[107],"those":[108],"both":[110,152],"non-sensitive":[113],"features.":[114],"This":[115],"enables":[116],"transparent,":[118],"feature-level":[119],"interpretation":[120],"interventions":[123],"reveals":[125],"may":[128],"persist":[129],"shift":[131],"through":[132],"correlated":[133],"variables.":[134],"requires":[137],"no":[138],"retraining":[140],"provides":[142],"actionable":[143],"insights":[144],"auditing":[147],"mitigation.":[149],"Experiments":[150],"synthetic":[153],"real-world":[155],"datasets":[156],"demonstrate":[157],"our":[159],"captures":[161],"dynamics":[163],"missed":[164],"by":[165],"prior":[166],"work,":[167],"offering":[168],"interpretable":[172],"tool":[173],"responsible":[175],"deployment":[176],"models.":[179]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-03-18T00:00:00"}
