{"id":"https://openalex.org/W4366549287","doi":"https://doi.org/10.1145/3544548.3581373","title":"ESCAPE: Countering Systematic Errors from Machine\u2019s Blind Spots via Interactive Visual Analysis","display_name":"ESCAPE: Countering Systematic Errors from Machine\u2019s Blind Spots via Interactive Visual Analysis","publication_year":2023,"publication_date":"2023-04-19","ids":{"openalex":"https://openalex.org/W4366549287","doi":"https://doi.org/10.1145/3544548.3581373"},"language":"en","primary_location":{"id":"doi:10.1145/3544548.3581373","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3544548.3581373","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems","raw_type":"proceedings-article"},"type":"article","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/A5040410232","display_name":"Yongsu Ahn","orcid":"https://orcid.org/0000-0002-5797-5445"},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yongsu Ahn","raw_affiliation_strings":["School of Computing and Information, University of Pittsburgh, United States"],"raw_orcid":"https://orcid.org/0000-0002-5797-5445","affiliations":[{"raw_affiliation_string":"School of Computing and Information, University of Pittsburgh, United States","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042159546","display_name":"Yu\u2010Ru Lin","orcid":"https://orcid.org/0000-0002-8497-3015"},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yu-Ru Lin","raw_affiliation_strings":["School of Computing and Information, University of Pittsburgh, United States"],"raw_orcid":"https://orcid.org/0000-0002-8497-3015","affiliations":[{"raw_affiliation_string":"School of Computing and Information, University of Pittsburgh, United States","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024059541","display_name":"Panpan Xu","orcid":"https://orcid.org/0000-0001-7966-2389"},"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":"Panpan Xu","raw_affiliation_strings":["Amazon AWS, United States"],"raw_orcid":"https://orcid.org/0000-0001-7966-2389","affiliations":[{"raw_affiliation_string":"Amazon AWS, United States","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036084788","display_name":"Zeng Dai","orcid":"https://orcid.org/0000-0001-9613-9434"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng Dai","raw_affiliation_strings":["Meta, United States"],"raw_orcid":"https://orcid.org/0000-0001-9613-9434","affiliations":[{"raw_affiliation_string":"Meta, United States","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5040410232"],"corresponding_institution_ids":["https://openalex.org/I170201317"],"apc_list":null,"apc_paid":null,"fwci":1.1775,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.8052126,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"16"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10799","display_name":"Data Visualization and Analytics","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.9908999800682068,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9907000064849854,"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/spurious-relationship","display_name":"Spurious relationship","score":0.9394536018371582},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.786605954170227},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5942797064781189},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.5867469310760498},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.557166337966919},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.505840539932251},{"id":"https://openalex.org/keywords/association","display_name":"Association (psychology)","score":0.41720470786094666},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4068608283996582}],"concepts":[{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.9394536018371582},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.786605954170227},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5942797064781189},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.5867469310760498},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.557166337966919},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.505840539932251},{"id":"https://openalex.org/C142853389","wikidata":"https://www.wikidata.org/wiki/Q744778","display_name":"Association (psychology)","level":2,"score":0.41720470786094666},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4068608283996582},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3544548.3581373","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3544548.3581373","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W2012457675","https://openalex.org/W2031489346","https://openalex.org/W2059362837","https://openalex.org/W2070967917","https://openalex.org/W2194775991","https://openalex.org/W2512274390","https://openalex.org/W2583689529","https://openalex.org/W2611211287","https://openalex.org/W2752332392","https://openalex.org/W2753044861","https://openalex.org/W2788428929","https://openalex.org/W2808158873","https://openalex.org/W2886614482","https://openalex.org/W2889624842","https://openalex.org/W2921712231","https://openalex.org/W2956281901","https://openalex.org/W2963795072","https://openalex.org/W2967579878","https://openalex.org/W2969670093","https://openalex.org/W2973221207","https://openalex.org/W2988437828","https://openalex.org/W2999004052","https://openalex.org/W3004725381","https://openalex.org/W3012736183","https://openalex.org/W3023163568","https://openalex.org/W3094294413","https://openalex.org/W3113107791","https://openalex.org/W3120502058","https://openalex.org/W3182543254","https://openalex.org/W3202452289","https://openalex.org/W3203358361","https://openalex.org/W3204393347","https://openalex.org/W3206420877","https://openalex.org/W3212513908","https://openalex.org/W3216878186","https://openalex.org/W4221146508","https://openalex.org/W4295990746","https://openalex.org/W4297460532","https://openalex.org/W4299828299","https://openalex.org/W4306176951","https://openalex.org/W4313429401"],"related_works":["https://openalex.org/W3113091479","https://openalex.org/W2162899405","https://openalex.org/W941090075","https://openalex.org/W2044987316","https://openalex.org/W3134374554","https://openalex.org/W2237480245","https://openalex.org/W2075065631","https://openalex.org/W2519167559","https://openalex.org/W4311248832","https://openalex.org/W4386113923"],"abstract_inverted_index":{"Classification":[0],"models":[1],"learn":[2],"to":[3,25,34,80,112,121,145,158],"generalize":[4],"the":[5,86,117,148,164],"associations":[6,149],"between":[7,150],"data":[8],"samples":[9,48],"and":[10,84,127,153,155,171,184],"their":[11],"target":[12],"classes.":[13],"However,":[14],"researchers":[15],"have":[16],"increasingly":[17],"observed":[18],"that":[19,99,131],"machine":[20],"learning":[21],"practice":[22],"easily":[23,113],"leads":[24],"systematic":[26,106],"errors":[27],"in":[28],"AI":[29],"applications,":[30],"a":[31,42,71,95,101,151],"phenomenon":[32],"referred":[33],"as":[35],"\u201cAI":[36],"blindspots.\u201d":[37],"Such":[38],"blindspots":[39],"arise":[40],"when":[41],"model":[43],"is":[44],"trained":[45],"with":[46,65],"training":[47],"(e.g.,":[49,55,63],"cat/dog":[50],"classification)":[51],"where":[52],"important":[53],"patterns":[54,62],"black":[56],"cats)":[57],"are":[58,68],"missing":[59],"or":[60],"periphery/undesirable":[61],"dogs":[64],"grass":[66],"background)":[67],"misleading":[69],"towards":[70],"certain":[72],"class.":[73],"Even":[74],"more":[75],"sophisticated":[76],"techniques":[77],"cannot":[78],"guarantee":[79],"capture,":[81],"reason":[82],"about,":[83],"prevent":[85],"spurious":[87,115,160],"associations.":[88,135,161],"In":[89],"this":[90],"work,":[91],"we":[92],"propose":[93,138],"ESCAPE,":[94],"visual":[96],"analytic":[97],"system":[98,118,170],"promotes":[100],"human-in-the-loop":[102],"workflow":[103],"for":[104],"countering":[105],"errors.":[107],"By":[108],"allowing":[109],"human":[110],"users":[111,120],"inspect":[114],"associations,":[116],"facilitates":[119],"spontaneously":[122],"recognize":[123],"concepts":[124],"associated":[125],"misclassifications":[126],"evaluate":[128],"mitigation":[129],"strategies":[130],"can":[132],"reduce":[133],"biased":[134],"We":[136,162],"also":[137],"two":[139],"statistical":[140,172],"approaches,":[141],"relative":[142],"concept":[143,152],"association":[144],"better":[146],"quantify":[147],"instances,":[154],"debias":[156],"method":[157],"mitigate":[159],"demonstrate":[163],"utility":[165],"of":[166],"our":[167],"proposed":[168],"ESCAPE":[169],"measures":[173],"through":[174],"extensive":[175],"evaluation":[176],"including":[177],"quantitative":[178],"experiments,":[179],"usage":[180],"scenarios,":[181],"expert":[182],"interviews,":[183],"controlled":[185],"user":[186],"experiments.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}