{"id":"https://openalex.org/W4394769180","doi":"https://doi.org/10.1145/3597503.3639181","title":"MAFT: Efficient Model-Agnostic Fairness Testing for Deep Neural Networks via Zero-Order Gradient Search","display_name":"MAFT: Efficient Model-Agnostic Fairness Testing for Deep Neural Networks via Zero-Order Gradient Search","publication_year":2024,"publication_date":"2024-04-12","ids":{"openalex":"https://openalex.org/W4394769180","doi":"https://doi.org/10.1145/3597503.3639181"},"language":"en","primary_location":{"id":"doi:10.1145/3597503.3639181","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3597503.3639181","pdf_url":null,"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":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2412.20086","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103180425","display_name":"Zhaohui Wang","orcid":"https://orcid.org/0009-0002-7774-5206"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaohui Wang","raw_affiliation_strings":["East China Normal University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0002-7774-5206","affiliations":[{"raw_affiliation_string":"East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100402899","display_name":"Min Zhang","orcid":"https://orcid.org/0000-0002-3152-4347"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Zhang","raw_affiliation_strings":["East China Normal University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-3152-4347","affiliations":[{"raw_affiliation_string":"East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052666402","display_name":"Jingran Yang","orcid":"https://orcid.org/0009-0008-6406-9222"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingran Yang","raw_affiliation_strings":["East China Normal University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0008-6406-9222","affiliations":[{"raw_affiliation_string":"East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085695710","display_name":"Bojie Shao","orcid":"https://orcid.org/0009-0005-9768-6970"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bojie Shao","raw_affiliation_strings":["East China Normal University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0005-9768-6970","affiliations":[{"raw_affiliation_string":"East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100402983","display_name":"Min Zhang","orcid":"https://orcid.org/0000-0003-1938-2902"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Zhang","raw_affiliation_strings":["East China Normal University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-1938-2902","affiliations":[{"raw_affiliation_string":"East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8328,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.86854179,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9970999956130981,"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":0.9926000237464905,"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":0.827709436416626},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7430172562599182},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.6744762659072876},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.6512458920478821},{"id":"https://openalex.org/keywords/white-box","display_name":"White box","score":0.5605526566505432},{"id":"https://openalex.org/keywords/black-box","display_name":"Black box","score":0.530953586101532},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49052804708480835},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43372076749801636},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4108802378177643},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3712526261806488},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34336644411087036},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3356889486312866}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.827709436416626},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7430172562599182},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.6744762659072876},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.6512458920478821},{"id":"https://openalex.org/C180932941","wikidata":"https://www.wikidata.org/wiki/Q997233","display_name":"White box","level":2,"score":0.5605526566505432},{"id":"https://openalex.org/C94966114","wikidata":"https://www.wikidata.org/wiki/Q29256","display_name":"Black box","level":2,"score":0.530953586101532},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49052804708480835},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43372076749801636},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4108802378177643},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3712526261806488},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34336644411087036},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3356889486312866},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3597503.3639181","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3597503.3639181","pdf_url":null,"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"},{"id":"pmh:oai:arXiv.org:2412.20086","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2412.20086","pdf_url":"https://arxiv.org/pdf/2412.20086","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2412.20086","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2412.20086","pdf_url":"https://arxiv.org/pdf/2412.20086","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.6399999856948853,"display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G6956063465","display_name":null,"funder_award_id":"62161146001","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7611692203","display_name":null,"funder_award_id":"22510750100","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322252","display_name":"Israel Science Foundation","ror":"https://ror.org/04sazxf24"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4394769180.pdf","grobid_xml":"https://content.openalex.org/works/W4394769180.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W1981276685","https://openalex.org/W2027461913","https://openalex.org/W2074466695","https://openalex.org/W2097246321","https://openalex.org/W2150593711","https://openalex.org/W2180612164","https://openalex.org/W2559655401","https://openalex.org/W2584805976","https://openalex.org/W2586702902","https://openalex.org/W2730550703","https://openalex.org/W2746600820","https://openalex.org/W2765982206","https://openalex.org/W2774644650","https://openalex.org/W2793714280","https://openalex.org/W2894732341","https://openalex.org/W2905810301","https://openalex.org/W2967682612","https://openalex.org/W3002398329","https://openalex.org/W3035447285","https://openalex.org/W3035671939","https://openalex.org/W3037696812","https://openalex.org/W3101686649","https://openalex.org/W3106412272","https://openalex.org/W3111294584","https://openalex.org/W3175660618","https://openalex.org/W3179976352","https://openalex.org/W3205573399","https://openalex.org/W4206579740","https://openalex.org/W4284697101","https://openalex.org/W4284709622","https://openalex.org/W4300511536","https://openalex.org/W6684072790","https://openalex.org/W6719080892","https://openalex.org/W6720710635","https://openalex.org/W6748377460","https://openalex.org/W6754546650","https://openalex.org/W6759580348","https://openalex.org/W6807593720","https://openalex.org/W7014198846"],"related_works":["https://openalex.org/W2047881532","https://openalex.org/W1984273188","https://openalex.org/W2727407240","https://openalex.org/W154189287","https://openalex.org/W3033197410","https://openalex.org/W2777690624","https://openalex.org/W2601181618","https://openalex.org/W2896078964","https://openalex.org/W4312601715","https://openalex.org/W4281399026"],"abstract_inverted_index":{"Deep":[0],"neural":[1],"networks":[2],"(DNNs)":[3],"have":[4,36],"shown":[5],"powerful":[6],"performance":[7,49,155],"in":[8,16,23,87,156],"various":[9],"applications":[10],"and":[11,47,84,107,122,164],"are":[12],"increasingly":[13],"being":[14],"used":[15],"decisionmaking":[17],"systems.":[18],"However,":[19],"concerns":[20],"about":[21,33],"fairness":[22,30,35,69,158],"DNNs":[24],"always":[25],"persist.":[26],"Some":[27],"efficient":[28],"white-box":[29,58,137],"testing":[31,70],"methods":[32,44,52,138],"individual":[34,68],"been":[37],"proposed.":[38],"Nevertheless,":[39],"the":[40,48,92,132,141],"development":[41],"of":[42,50,57,91],"black-box":[43,67,149],"has":[45],"stagnated,":[46],"existing":[51,125,148],"is":[53],"far":[54],"behind":[55],"that":[56,129],"methods.":[59,126],"In":[60],"this":[61],"paper,":[62],"we":[63],"propose":[64],"a":[65],"novel":[66],"method":[71],"called":[72],"Model-Agnostic":[73],"Fairness":[74],"Testing":[75],"(MAFT).":[76],"By":[77],"leveraging":[78],"MAFT,":[79],"practitioners":[80],"can":[81],"effectively":[82],"identify":[83],"address":[85],"discrimination":[86],"DL":[88],"models,":[89],"regardless":[90],"specific":[93],"algorithm":[94],"or":[95],"architecture":[96],"employed.":[97],"Our":[98],"approach":[99,152],"adopts":[100],"lightweight":[101],"procedures":[102,113],"such":[103],"as":[104,135],"gradient":[105],"estimation":[106],"attribute":[108],"perturbation":[109],"rather":[110],"than":[111,124],"non-trivial":[112],"like":[114],"symbol":[115],"execution,":[116],"rendering":[117],"it":[118],"significantly":[119],"more":[120],"scalable":[121],"applicable":[123],"We":[127],"demonstrate":[128],"MAFT":[130],"achieves":[131],"same":[133],"effectiveness":[134,161],"state-of-the-art":[136],"whilst":[139],"improving":[140],"applicability":[142],"to":[143,147],"large-scale":[144],"networks.":[145],"Compared":[146],"approaches,":[150],"our":[151],"demonstrates":[153],"distinguished":[154],"discovering":[157],"violations":[159],"w.r.t":[160],"(~":[162,166],"14.69\u00d7)":[163],"efficiency":[165],"32.58\u00d7).":[167]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
