{"id":"https://openalex.org/W4391579668","doi":"https://doi.org/10.1145/3597503.3623334","title":"RUNNER: Responsible UNfair NEuron Repair for Enhancing Deep Neural Network Fairness","display_name":"RUNNER: Responsible UNfair NEuron Repair for Enhancing Deep Neural Network Fairness","publication_year":2024,"publication_date":"2024-02-06","ids":{"openalex":"https://openalex.org/W4391579668","doi":"https://doi.org/10.1145/3597503.3623334"},"language":"en","primary_location":{"id":"doi:10.1145/3597503.3623334","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3597503.3623334","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3597503.3623334","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the IEEE/ACM 46th International Conference on Software Engineering","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3597503.3623334","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101674538","display_name":"Tianlin Li","orcid":"https://orcid.org/0000-0002-2207-1622"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Tianlin Li","raw_affiliation_strings":["Nanyang Technological University, Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0002-2207-1622","affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039207695","display_name":"Yue Cao","orcid":"https://orcid.org/0009-0001-3785-7281"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yue Cao","raw_affiliation_strings":["Nanyang Technological University, Singapore, Singapore"],"raw_orcid":"https://orcid.org/0009-0001-3785-7281","affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035797029","display_name":"Jian Zhang","orcid":"https://orcid.org/0000-0001-8316-1894"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Jian Zhang","raw_affiliation_strings":["Nanyang Technological University, Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0001-8316-1894","affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004356886","display_name":"Shiqian Zhao","orcid":"https://orcid.org/0009-0003-1337-2305"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Shiqian Zhao","raw_affiliation_strings":["Nanyang Technological University, Singapore, Singapore"],"raw_orcid":"https://orcid.org/0009-0003-1337-2305","affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036183844","display_name":"Yihao Huang","orcid":"https://orcid.org/0000-0002-5784-770X"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yihao Huang","raw_affiliation_strings":["Nanyang Technological University, Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0002-5784-770X","affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014870180","display_name":"Aishan Liu","orcid":"https://orcid.org/0000-0002-4224-1318"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Aishan Liu","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4224-1318","affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026239594","display_name":"Qing Guo","orcid":"https://orcid.org/0000-0003-0974-9299"},"institutions":[{"id":"https://openalex.org/I115228651","display_name":"Agency for Science, Technology and Research","ror":"https://ror.org/036wvzt09","country_code":"SG","type":"government","lineage":["https://openalex.org/I115228651"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Qing Guo","raw_affiliation_strings":["ASTAR, Singapore, China"],"raw_orcid":"https://orcid.org/0000-0003-0974-9299","affiliations":[{"raw_affiliation_string":"ASTAR, Singapore, China","institution_ids":["https://openalex.org/I115228651"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100355692","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0001-7300-9215"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yang Liu","raw_affiliation_strings":["Nanyang Technological University, Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0001-7300-9215","affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":11,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"13"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9994000196456909,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9994000196456909,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.9983999729156494,"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":0.9865000247955322,"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":0.8062648773193359},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.7306824922561646},{"id":"https://openalex.org/keywords/retraining","display_name":"Retraining","score":0.6358402967453003},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.6348371505737305},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48435983061790466},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.45889046788215637},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4509373605251312},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4188157618045807},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.243938148021698}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8062648773193359},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.7306824922561646},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.6358402967453003},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.6348371505737305},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48435983061790466},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.45889046788215637},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4509373605251312},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4188157618045807},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.243938148021698},{"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/C155202549","wikidata":"https://www.wikidata.org/wiki/Q178803","display_name":"International trade","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3597503.3623334","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3597503.3623334","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3597503.3623334","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the IEEE/ACM 46th International Conference on Software Engineering","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3597503.3623334","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3597503.3623334","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3597503.3623334","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the IEEE/ACM 46th International Conference on Software Engineering","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.5799999833106995,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G1958612786","display_name":null,"funder_award_id":"AISG2-RP-2020-019","funder_id":"https://openalex.org/F4320320671","funder_display_name":"National Research Foundation"},{"id":"https://openalex.org/G4382255046","display_name":null,"funder_award_id":"NRF2018NCR-NSOE003-0001","funder_id":"https://openalex.org/F4320320709","funder_display_name":"National Research Foundation Singapore"},{"id":"https://openalex.org/G478423709","display_name":null,"funder_award_id":"AISG2-RP-2020-019","funder_id":"https://openalex.org/F4320320709","funder_display_name":"National Research Foundation Singapore"},{"id":"https://openalex.org/G489730147","display_name":null,"funder_award_id":"62206009","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6828898563","display_name":null,"funder_award_id":"NRF2018NCR-NSOE003-0001","funder_id":"https://openalex.org/F4320320671","funder_display_name":"National Research Foundation"},{"id":"https://openalex.org/G7822712350","display_name":null,"funder_award_id":"NRF-NRFI06-2020-0001","funder_id":"https://openalex.org/F4320320709","funder_display_name":"National Research Foundation Singapore"},{"id":"https://openalex.org/G8983560273","display_name":null,"funder_award_id":"NRF-NRFI06-2020-0001","funder_id":"https://openalex.org/F4320320671","funder_display_name":"National Research Foundation"}],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320320709","display_name":"National Research Foundation Singapore","ror":"https://ror.org/03cpyc314"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4391579668.pdf","grobid_xml":"https://content.openalex.org/works/W4391579668.grobid-xml"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W62087154","https://openalex.org/W1554663460","https://openalex.org/W1787224781","https://openalex.org/W1834627138","https://openalex.org/W1979769549","https://openalex.org/W2002771657","https://openalex.org/W2014352947","https://openalex.org/W2100960835","https://openalex.org/W2194775991","https://openalex.org/W2293624369","https://openalex.org/W2550080458","https://openalex.org/W2904642422","https://openalex.org/W2955426500","https://openalex.org/W2962866211","https://openalex.org/W2963116854","https://openalex.org/W2964023221","https://openalex.org/W3002398329","https://openalex.org/W3007501395","https://openalex.org/W3034700241","https://openalex.org/W3035447285","https://openalex.org/W3082949018","https://openalex.org/W3090119274","https://openalex.org/W3099610985","https://openalex.org/W3116194568","https://openalex.org/W3137991047","https://openalex.org/W3154335119","https://openalex.org/W3165292502","https://openalex.org/W3165435112","https://openalex.org/W3176798257","https://openalex.org/W3177313640","https://openalex.org/W3193448347","https://openalex.org/W3206100932","https://openalex.org/W4210455774","https://openalex.org/W4232172926","https://openalex.org/W4284681038","https://openalex.org/W4284709622","https://openalex.org/W4288083801","https://openalex.org/W4380303848","https://openalex.org/W4385767365","https://openalex.org/W4386136237","https://openalex.org/W6684642658","https://openalex.org/W6798345263","https://openalex.org/W6912427866"],"related_works":["https://openalex.org/W2595172197","https://openalex.org/W2084856301","https://openalex.org/W2127970246","https://openalex.org/W2081982437","https://openalex.org/W2885125400","https://openalex.org/W1001352512","https://openalex.org/W1989889224","https://openalex.org/W4382618745","https://openalex.org/W1973775000","https://openalex.org/W2027050655"],"abstract_inverted_index":{"Deep":[0],"Neural":[1],"Networks":[2],"(DNNs),":[3],"an":[4,62],"emerging":[5],"software":[6],"technology,":[7],"have":[8],"achieved":[9],"impressive":[10],"results":[11,257],"in":[12,35,135,152,187,210],"a":[13,31,50,109,156,172],"variety":[14],"of":[15,26,52,59,160,180],"fields.":[16],"However,":[17],"the":[18,67,143,166,177,194,229,259],"discriminatory":[19],"behaviors":[20],"towards":[21],"certain":[22],"groups":[23],"(a.k.a.":[24],"unfairness)":[25],"DNN":[27],"models":[28],"increasingly":[29],"become":[30],"social":[32],"concern,":[33],"especially":[34],"high-stake":[36],"applications":[37],"such":[38],"as":[39],"loan":[40],"approval":[41],"and":[42,86,104,201,224,227,245],"criminal":[43],"risk":[44],"assessment.":[45],"Although":[46],"there":[47],"has":[48],"been":[49],"number":[51],"works":[53,134],"to":[54,64,99,113,243,250],"improve":[55],"model":[56,68],"fairness,":[57],"most":[58],"them":[60,88],"adopt":[61],"adversary":[63],"either":[65],"expand":[66],"architecture":[69],"or":[70],"augment":[71],"training":[72,101],"data,":[73,205],"which":[74,206],"introduces":[75],"excessive":[76],"computational":[77],"overhead.":[78],"Recent":[79],"work":[80],"diagnoses":[81],"responsible":[82,149,181],"unfair":[83,116,150,182],"neurons":[84,117,151,183],"first":[85],"fixes":[87],"with":[89,155],"selective":[90,105],"retraining.":[91],"Unfortunately,":[92],"existing":[93,133],"diagnosis":[94],"process":[95],"is":[96,207],"time-consuming":[97],"due":[98,112],"multi-step":[100],"sample":[102],"analysis,":[103],"retraining":[106],"may":[107],"cause":[108],"performance":[110],"bottleneck":[111],"indirectly":[114],"adjusting":[115],"on":[118,196,258],"biased":[119],"samples.":[120],"In":[121],"this":[122],"paper,":[123],"we":[124,141,164,192],"propose":[125],"Responsible":[126],"UNfair":[127],"NEuron":[128],"Repair":[129],"(RUNNER)":[130],"that":[131,147,175,220],"improves":[132],"three":[136],"key":[137],"aspects:":[138],"(1)":[139],"efficiency:":[140],"design":[142,165],"Importance-based":[144],"Neuron":[145,167],"Diagnosis":[146],"identifies":[148],"one":[153],"step":[154],"novel":[157],"importance":[158],"criterion":[159],"neurons;":[161],"(2)":[162],"effectiveness:":[163],"Stabilizing":[168],"Retraining":[169],"by":[170],"adding":[171],"loss":[173],"term":[174],"measures":[176],"activation":[178],"distance":[179],"from":[184,241],"different":[185],"subgroups":[186],"all":[188],"sources;":[189],"(3)":[190],"generalization:":[191],"investigate":[193],"effectiveness":[195],"both":[197],"structured":[198],"tabular":[199],"data":[200],"large-scale":[202],"unstructured":[203,260],"image":[204],"often":[208],"ignored":[209],"prior":[211],"studies.":[212],"Our":[213],"extensive":[214],"experiments":[215],"across":[216],"5":[217],"datasets":[218],"show":[219],"RUUNER":[221],"can":[222],"effectively":[223],"efficiently":[225],"diagnose":[226],"repair":[228],"DNNs":[230],"regarding":[231],"unfairness.":[232],"On":[233],"average,":[234],"our":[235],"approach":[236],"significantly":[237],"reduces":[238],"computing":[239],"overhead":[240],"341.7s":[242],"29.65s,":[244],"achieves":[246],"improved":[247],"fairness":[248],"up":[249],"79.3%.":[251],"Besides,":[252],"RUNNER":[253],"also":[254],"keeps":[255],"state-of-the-art":[256],"dataset.":[261]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
