{"id":"https://openalex.org/W4386242375","doi":"https://doi.org/10.1145/3600211.3604713","title":"Stress-Testing Bias Mitigation Algorithms to Understand Fairness Vulnerabilities","display_name":"Stress-Testing Bias Mitigation Algorithms to Understand Fairness Vulnerabilities","publication_year":2023,"publication_date":"2023-08-08","ids":{"openalex":"https://openalex.org/W4386242375","doi":"https://doi.org/10.1145/3600211.3604713"},"language":"en","primary_location":{"id":"doi:10.1145/3600211.3604713","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3600211.3604713","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3600211.3604713","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3600211.3604713","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070426983","display_name":"Karan Bhanot","orcid":"https://orcid.org/0000-0003-4791-5796"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Karan Bhanot","raw_affiliation_strings":["Rensselaer Polytechnic Institute, USA"],"raw_orcid":"https://orcid.org/0000-0003-4791-5796","affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute, USA","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073341059","display_name":"Ioana Baldini","orcid":"https://orcid.org/0000-0002-8257-9866"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ioana Baldini","raw_affiliation_strings":["IBM Research, USA"],"raw_orcid":"https://orcid.org/0000-0002-8257-9866","affiliations":[{"raw_affiliation_string":"IBM Research, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103053820","display_name":"Dennis Wei","orcid":"https://orcid.org/0000-0002-6510-1537"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dennis Wei","raw_affiliation_strings":["IBM Research, USA"],"raw_orcid":"https://orcid.org/0000-0002-6510-1537","affiliations":[{"raw_affiliation_string":"IBM Research, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041049334","display_name":"Jiaming Zeng","orcid":"https://orcid.org/0000-0003-2927-5916"},"institutions":[{"id":"https://openalex.org/I10734018","display_name":"Akamai (United States)","ror":"https://ror.org/03tarb191","country_code":"US","type":"company","lineage":["https://openalex.org/I10734018"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiaming Zeng","raw_affiliation_strings":["AKASA, USA"],"raw_orcid":"https://orcid.org/0000-0003-2927-5916","affiliations":[{"raw_affiliation_string":"AKASA, USA","institution_ids":["https://openalex.org/I10734018"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048876983","display_name":"Kristin P. Bennett","orcid":"https://orcid.org/0000-0002-8782-105X"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kristin Bennett","raw_affiliation_strings":["Rensselaer Polytechnic Institute, USA"],"raw_orcid":"https://orcid.org/0000-0002-8782-105X","affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute, USA","institution_ids":["https://openalex.org/I165799507"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5070426983"],"corresponding_institution_ids":["https://openalex.org/I165799507"],"apc_list":null,"apc_paid":null,"fwci":0.7565,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.77287339,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"764","last_page":"774"},"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":0.9983999729156494,"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"}},"topics":[{"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":0.9869999885559082,"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":0.9868000149726868,"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":0.7869064807891846},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6067758798599243},{"id":"https://openalex.org/keywords/audit","display_name":"Audit","score":0.5652474164962769},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5520567893981934},{"id":"https://openalex.org/keywords/fairness-measure","display_name":"Fairness measure","score":0.5333951115608215},{"id":"https://openalex.org/keywords/trustworthiness","display_name":"Trustworthiness","score":0.5316717624664307},{"id":"https://openalex.org/keywords/stress-testing","display_name":"Stress testing (software)","score":0.5183318853378296},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5066757798194885},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.42525044083595276},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40241438150405884},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3537599742412567},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.16952520608901978}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7869064807891846},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6067758798599243},{"id":"https://openalex.org/C199521495","wikidata":"https://www.wikidata.org/wiki/Q181487","display_name":"Audit","level":2,"score":0.5652474164962769},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5520567893981934},{"id":"https://openalex.org/C11867375","wikidata":"https://www.wikidata.org/wiki/Q5430671","display_name":"Fairness measure","level":4,"score":0.5333951115608215},{"id":"https://openalex.org/C153701036","wikidata":"https://www.wikidata.org/wiki/Q659974","display_name":"Trustworthiness","level":2,"score":0.5316717624664307},{"id":"https://openalex.org/C7515471","wikidata":"https://www.wikidata.org/wiki/Q1936882","display_name":"Stress testing (software)","level":2,"score":0.5183318853378296},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5066757798194885},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.42525044083595276},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40241438150405884},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3537599742412567},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.16952520608901978},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"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/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3600211.3604713","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3600211.3604713","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3600211.3604713","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3600211.3604713","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3600211.3604713","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3600211.3604713","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.5899999737739563}],"awards":[],"funders":[{"id":"https://openalex.org/F4320309500","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4386242375.pdf","grobid_xml":"https://content.openalex.org/works/W4386242375.grobid-xml"},"referenced_works_count":13,"referenced_works":["https://openalex.org/W2014352947","https://openalex.org/W2116666691","https://openalex.org/W2162800060","https://openalex.org/W2178457908","https://openalex.org/W2396881363","https://openalex.org/W2560674852","https://openalex.org/W2959197226","https://openalex.org/W3015471667","https://openalex.org/W3104728961","https://openalex.org/W3165211483","https://openalex.org/W4221146562","https://openalex.org/W6888840370","https://openalex.org/W6903655558"],"related_works":["https://openalex.org/W2076536433","https://openalex.org/W90316445","https://openalex.org/W2361713743","https://openalex.org/W4327743613","https://openalex.org/W2965447900","https://openalex.org/W3199750033","https://openalex.org/W2374509987","https://openalex.org/W3163373470","https://openalex.org/W3037187668","https://openalex.org/W2147625294"],"abstract_inverted_index":{"To":[0,41],"address":[1,42],"the":[2,46,88,106,111,142],"growing":[3],"concern":[4],"of":[5,49,116],"unfairness":[6],"in":[7,18],"Artificial":[8],"Intelligence":[9],"(AI),":[10],"several":[11,53],"bias":[12,94],"mitigation":[13,95,137],"algorithms":[14,51,65,96,118,138],"have":[15],"been":[16],"introduced":[17],"prior":[19],"research.":[20],"Their":[21],"capabilities":[22],"are":[23],"often":[24],"evaluated":[25],"on":[26],"certain":[27],"overly-used":[28],"datasets":[29,100],"without":[30],"rigorously":[31],"stress-testing":[32],"them":[33],"under":[34,119],"simultaneous":[35],"train":[36],"and":[37,67,113,139],"test":[38],"distribution":[39,54],"shifts.":[40,102,121],"this,":[43],"we":[44,82,104,123],"investigate":[45],"fairness":[47,112],"vulnerabilities":[48],"these":[50,64,117],"across":[52,133],"shift":[55],"scenarios":[56,62],"using":[57,97],"synthetic":[58,99,135],"data,":[59],"to":[60,70,91],"highlight":[61],"where":[63],"do":[66],"don\u2019t":[68],"work":[69],"encourage":[71],"their":[72],"trustworthy":[73],"use.":[74],"The":[75],"paper":[76],"makes":[77],"three":[78],"important":[79],"contributions.":[80],"Firstly,":[81],"propose":[83,124],"a":[84],"flexible":[85],"pipeline":[86],"called":[87,141],"Fairness":[89,143],"Auditor":[90],"systematically":[92],"stress-test":[93],"multiple":[98],"with":[101],"Secondly,":[103],"introduce":[105],"Deviation":[107],"Metric":[108],"for":[109,129],"measuring":[110],"utility":[114],"performance":[115,132],"such":[120],"Thirdly,":[122],"an":[125],"interactive":[126],"reporting":[127],"tool":[128],"comparing":[130],"algorithmic":[131],"various":[134],"datasets,":[136],"metrics":[140],"Report.":[144]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}