{"id":"https://openalex.org/W4402442690","doi":"https://doi.org/10.1145/3650212.3680380","title":"NeuFair: Neural Network Fairness Repair with Dropout","display_name":"NeuFair: Neural Network Fairness Repair with Dropout","publication_year":2024,"publication_date":"2024-09-11","ids":{"openalex":"https://openalex.org/W4402442690","doi":"https://doi.org/10.1145/3650212.3680380"},"language":"en","primary_location":{"id":"doi:10.1145/3650212.3680380","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3650212.3680380","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis","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/A5065558612","display_name":"Vishnu Asutosh Dasu","orcid":"https://orcid.org/0000-0002-1849-1288"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vishnu Asutosh Dasu","raw_affiliation_strings":["Pennsylvania State University, State College, USA"],"raw_orcid":"https://orcid.org/0000-0002-1849-1288","affiliations":[{"raw_affiliation_string":"Pennsylvania State University, State College, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101937116","display_name":"Ashish Kumar","orcid":"https://orcid.org/0000-0001-8773-2084"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ashish Kumar","raw_affiliation_strings":["Pennsylvania State University, State College, USA"],"raw_orcid":"https://orcid.org/0000-0001-8773-2084","affiliations":[{"raw_affiliation_string":"Pennsylvania State University, State College, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076300752","display_name":"Saeid Tizpaz-Niari","orcid":"https://orcid.org/0000-0002-1375-3154"},"institutions":[{"id":"https://openalex.org/I164936912","display_name":"The University of Texas at El Paso","ror":"https://ror.org/04d5vba33","country_code":"US","type":"education","lineage":["https://openalex.org/I164936912"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Saeid Tizpaz-Niari","raw_affiliation_strings":["University of Texas at El Paso, El Paso, USA"],"raw_orcid":"https://orcid.org/0000-0002-1375-3154","affiliations":[{"raw_affiliation_string":"University of Texas at El Paso, El Paso, USA","institution_ids":["https://openalex.org/I164936912"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010830558","display_name":"Gang Tan","orcid":"https://orcid.org/0000-0001-6109-6091"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gang Tan","raw_affiliation_strings":["Pennsylvania State University, State College, USA"],"raw_orcid":"https://orcid.org/0000-0001-6109-6091","affiliations":[{"raw_affiliation_string":"Pennsylvania State University, State College, USA","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.2004,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.93555268,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1541","last_page":"1553"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9991999864578247,"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":0.9991999864578247,"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":0.9940000176429749,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9857000112533569,"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/dropout","display_name":"Dropout (neural networks)","score":0.7422796487808228},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6005507707595825},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5884348750114441},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.33078527450561523},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.27210426330566406},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1423274278640747}],"concepts":[{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.7422796487808228},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6005507707595825},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5884348750114441},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.33078527450561523},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27210426330566406},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1423274278640747}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3650212.3680380","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3650212.3680380","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1979769549","https://openalex.org/W2014352947","https://openalex.org/W2063104570","https://openalex.org/W2095705004","https://openalex.org/W2148143831","https://openalex.org/W2183112036","https://openalex.org/W2730550703","https://openalex.org/W2809701591","https://openalex.org/W2913771223","https://openalex.org/W2953361516","https://openalex.org/W2963116854","https://openalex.org/W2967682612","https://openalex.org/W2991598122","https://openalex.org/W3002398329","https://openalex.org/W3027373771","https://openalex.org/W3103741452","https://openalex.org/W3105507623","https://openalex.org/W3156188980","https://openalex.org/W3165292502","https://openalex.org/W3166873126","https://openalex.org/W3179976352","https://openalex.org/W3193448347","https://openalex.org/W3194157648","https://openalex.org/W4220659214","https://openalex.org/W4224249875","https://openalex.org/W4230167402","https://openalex.org/W4282574503","https://openalex.org/W4284681038","https://openalex.org/W4284701600","https://openalex.org/W4284709622","https://openalex.org/W4308641598","https://openalex.org/W4308643061","https://openalex.org/W4308731725","https://openalex.org/W4312890174","https://openalex.org/W4384302771","https://openalex.org/W4384304645","https://openalex.org/W4386136237","https://openalex.org/W6686330987","https://openalex.org/W6967785845"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W3082178636","https://openalex.org/W2782041652","https://openalex.org/W2612657834","https://openalex.org/W2392157706","https://openalex.org/W2599192953","https://openalex.org/W2952088488","https://openalex.org/W1521968289","https://openalex.org/W4225691210"],"abstract_inverted_index":{"This":[0],"paper":[1],"investigates":[2],"neuron":[3],"dropout":[4,65],"as":[5],"a":[6,93,99],"post-processing":[7,102],"bias":[8,48,182],"mitigation":[9,49],"method":[10],"for":[11],"deep":[12],"neural":[13],"networks":[14],"(DNNs).":[15],"Neural-driven":[16],"software":[17],"solutions":[18],"are":[19,32],"increasingly":[20],"applied":[21],"in":[22,108,139],"socially":[23],"critical":[24],"domains":[25],"with":[26,145],"significant":[27],"fairness":[28,77,141],"implications.":[29],"While":[30],"DNNs":[31,80,110],"exceptional":[33],"at":[34],"learning":[35,59],"statistical":[36],"patterns":[37],"from":[38],"data,":[39],"they":[40],"may":[41,67],"encode":[42],"and":[43,71,137,159,175],"amplify":[44],"historical":[45],"biases.":[46],"Existing":[47],"algorithms":[50,104],"often":[51],"require":[52],"modifying":[53],"the":[54,58,85,128,162,170],"input":[55],"dataset":[56],"or":[57,147],"algorithms.":[60],"We":[61,96,131,152],"posit":[62],"that":[63,105,133],"prevalent":[64],"methods":[66],"be":[68],"an":[69,121],"effective":[70,138],"less":[72],"intrusive":[73],"approach":[74],"to":[75,90,123,143,179],"improve":[76],"of":[78,88,101,156,164,167],"pre-trained":[79,109],"during":[81,113],"inference.":[82,114],"However,":[83],"finding":[84],"ideal":[86],"set":[87],"neurons":[89],"drop":[91],"is":[92,118,135],"combinatorial":[94],"problem.":[95],"propose":[97],"NeuFair,":[98],"family":[100],"randomized":[103,116],"mitigate":[106],"unfairness":[107],"via":[111],"dropouts":[112],"Our":[115],"search":[117],"guided":[119],"by":[120],"objective":[122],"minimize":[124],"discrimination":[125],"while":[126],"maintaining":[127],"model\u2019s":[129],"utility.":[130],"show":[132],"NeuFair":[134,168,178],"efficient":[136],"improving":[140],"(up":[142],"69%)":[144],"minimal":[146],"no":[148],"model":[149],"performance":[150],"degradation.":[151],"provide":[153],"intuitive":[154],"explanations":[155],"these":[157],"phenomena":[158],"carefully":[160],"examine":[161],"influence":[163],"various":[165],"hyperparameters":[166],"on":[169],"results.":[171],"Finally,":[172],"we":[173],"empirically":[174],"conceptually":[176],"compare":[177],"different":[180],"state-of-the-art":[181],"mitigators.":[183]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":7}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
