{"id":"https://openalex.org/W4387846598","doi":"https://doi.org/10.1145/3583780.3614792","title":"Bias Invariant Approaches for Improving Word Embedding Fairness","display_name":"Bias Invariant Approaches for Improving Word Embedding Fairness","publication_year":2023,"publication_date":"2023-10-21","ids":{"openalex":"https://openalex.org/W4387846598","doi":"https://doi.org/10.1145/3583780.3614792"},"language":"en","primary_location":{"id":"doi:10.1145/3583780.3614792","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3614792","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3614792","source":null,"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","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/10.1145/3583780.3614792","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073101300","display_name":"Siyu Liao","orcid":"https://orcid.org/0000-0001-5709-3015"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Siyu Liao","raw_affiliation_strings":["Amazon.com, Seattle, WA, USA"],"affiliations":[{"raw_affiliation_string":"Amazon.com, Seattle, WA, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041106394","display_name":"Rongting Zhang","orcid":"https://orcid.org/0000-0002-2420-2438"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rongting Zhang","raw_affiliation_strings":["Amazon.com, Seattle, WA, USA"],"affiliations":[{"raw_affiliation_string":"Amazon.com, Seattle, WA, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009958753","display_name":"B\u00e1rbara Poblete","orcid":"https://orcid.org/0000-0002-7669-645X"},"institutions":[{"id":"https://openalex.org/I69737025","display_name":"University of Chile","ror":"https://ror.org/047gc3g35","country_code":"CL","type":"education","lineage":["https://openalex.org/I69737025"]}],"countries":["CL"],"is_corresponding":false,"raw_author_name":"Barbara Poblete","raw_affiliation_strings":["University of Chile & Amazon.com, Santiago, Chile"],"affiliations":[{"raw_affiliation_string":"University of Chile & Amazon.com, Santiago, Chile","institution_ids":["https://openalex.org/I69737025"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060492418","display_name":"Vanessa Murdock","orcid":"https://orcid.org/0000-0003-1682-0081"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vanessa Murdock","raw_affiliation_strings":["Amazon.com, Seattle, WA, USA"],"affiliations":[{"raw_affiliation_string":"Amazon.com, Seattle, WA, USA","institution_ids":["https://openalex.org/I1311688040"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5073101300"],"corresponding_institution_ids":["https://openalex.org/I1311688040"],"apc_list":null,"apc_paid":null,"fwci":0.3479,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.66674385,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1400","last_page":"1410"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","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/T10764","display_name":"Privacy-Preserving Technologies in Data","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/T11045","display_name":"Privacy, Security, and Data Protection","score":0.9839000105857849,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9311000108718872,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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":0.7734226584434509},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.6587811708450317},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.64236980676651},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6230783462524414},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5142311453819275},{"id":"https://openalex.org/keywords/differential-privacy","display_name":"Differential privacy","score":0.5090146064758301},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.4932236075401306},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46572965383529663},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.46079736948013306},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.44881904125213623},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.41970300674438477},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41212135553359985},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.37638694047927856},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.31718066334724426},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.24986904859542847},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13378781080245972}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7734226584434509},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.6587811708450317},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.64236980676651},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6230783462524414},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5142311453819275},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.5090146064758301},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.4932236075401306},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46572965383529663},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.46079736948013306},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.44881904125213623},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.41970300674438477},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41212135553359985},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.37638694047927856},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31718066334724426},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.24986904859542847},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13378781080245972},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3583780.3614792","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3614792","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3614792","source":null,"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3583780.3614792","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3614792","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3614792","source":null,"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.5299999713897705,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387846598.pdf","grobid_xml":"https://content.openalex.org/works/W4387846598.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W1497454515","https://openalex.org/W2025356718","https://openalex.org/W2027595342","https://openalex.org/W2144578941","https://openalex.org/W2165612380","https://openalex.org/W2187089797","https://openalex.org/W2250539671","https://openalex.org/W2470207857","https://openalex.org/W2473418344","https://openalex.org/W2515910099","https://openalex.org/W2550923470","https://openalex.org/W2561529111","https://openalex.org/W2595551253","https://openalex.org/W2612649659","https://openalex.org/W2747329762","https://openalex.org/W2769358515","https://openalex.org/W2798935874","https://openalex.org/W2891768540","https://openalex.org/W2897471207","https://openalex.org/W2952929029","https://openalex.org/W2954275542","https://openalex.org/W2963292194","https://openalex.org/W2963526187","https://openalex.org/W2979401726","https://openalex.org/W2997726715","https://openalex.org/W2998368506","https://openalex.org/W3035448343","https://openalex.org/W3037831233","https://openalex.org/W3083213769","https://openalex.org/W3116035283","https://openalex.org/W3184606595","https://openalex.org/W3195132180","https://openalex.org/W4231165370","https://openalex.org/W4287887636","https://openalex.org/W6657138077"],"related_works":["https://openalex.org/W2468279273","https://openalex.org/W3038283795","https://openalex.org/W2604501336","https://openalex.org/W2734500670","https://openalex.org/W2354198838","https://openalex.org/W2558166297","https://openalex.org/W1989130879","https://openalex.org/W2315671126","https://openalex.org/W2103419012","https://openalex.org/W798507144"],"abstract_inverted_index":{"Many":[0],"public":[1,148],"pre-trained":[2,149],"word":[3,72,118,150],"embeddings":[4],"have":[5],"been":[6],"shown":[7],"to":[8,70,90,147],"encode":[9],"different":[10,135],"types":[11,123],"of":[12,32,81,100,109,114,124,181],"biases.":[13],"Embeddings":[14],"are":[15],"often":[16],"obtained":[17],"from":[18,78,140],"training":[19,101],"on":[20,131],"large":[21],"pre-existing":[22],"corpora,":[23],"and":[24,53,127,173],"therefore":[25],"resulting":[26,183],"biases":[27,56,116],"can":[28],"be":[29],"a":[30,44,97],"reflection":[31],"unfair":[33],"representations":[34],"in":[35,40,57,117],"the":[36,58,79,106,179,182],"original":[37],"data.":[38],"Bias,":[39],"this":[41,66,87],"scenario,":[42],"is":[43,61,89],"challenging":[45],"problem":[46],"since":[47],"current":[48],"mitigation":[49],"techniques":[50,139,177],"require":[51],"knowing":[52],"understanding":[54],"existing":[55],"embedding,":[59],"which":[60],"not":[62,110],"always":[63],"possible.":[64],"In":[65],"work,":[67],"we":[68,143,156],"propose":[69],"improve":[71,167],"embedding":[73,184],"fairness":[74,168],"by":[75],"borrowing":[76],"methods":[77],"field":[80],"data":[82,102],"privacy.":[83],"The":[84],"idea":[85],"behind":[86],"approach":[88],"treat":[91],"bias":[92,132],"as":[93],"if":[94],"it":[95],"were":[96],"special":[98],"type":[99],"leakage.":[103],"This":[104],"has":[105],"unique":[107],"advantage":[108],"requiring":[111],"prior":[112],"knowledge":[113],"potential":[115],"embeddings.":[119,151],"We":[120],"investigated":[121],"two":[122],"privacy":[125,176],"algorithms,":[126],"measured":[128],"their":[129],"effect":[130],"using":[133],"four":[134],"metrics.":[136],"To":[137,152],"investigate":[138,153],"differential":[141],"privacy,":[142,155],"applied":[144,157],"Gaussian":[145],"perturbation":[146],"noiseless":[154,175],"vector":[158],"quantization":[159],"during":[160],"training.":[161],"Experiments":[162],"show":[163],"that":[164],"both":[165],"approaches":[166],"for":[169],"commonly":[170],"used":[171],"embeddings,":[172],"additionally,":[174],"reduce":[178],"size":[180],"representation.":[185]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}