{"id":"https://openalex.org/W4322716343","doi":"https://doi.org/10.48550/arxiv.2302.12893","title":"Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation","display_name":"Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation","publication_year":2023,"publication_date":"2023-02-24","ids":{"openalex":"https://openalex.org/W4322716343","doi":"https://doi.org/10.48550/arxiv.2302.12893","pmid":"https://pubmed.ncbi.nlm.nih.gov/40290784"},"language":"en","primary_location":{"id":"pmid:40290784","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40290784","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of machine learning research","raw_type":null},"type":"preprint","indexed_in":["arxiv","datacite","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2302.12893","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007411646","display_name":"Neil Jethani","orcid":"https://orcid.org/0000-0002-1364-7984"},"institutions":[{"id":"https://openalex.org/I36672615","display_name":"Courant Institute of Mathematical Sciences","ror":"https://ror.org/037tm7f56","country_code":"US","type":"education","lineage":["https://openalex.org/I36672615","https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jethani, Neil","raw_affiliation_strings":["Grossman School of Medicine, Courant Institute New York University"],"affiliations":[{"raw_affiliation_string":"Grossman School of Medicine, Courant Institute New York University","institution_ids":["https://openalex.org/I36672615"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084757090","display_name":"Adriel Saporta","orcid":"https://orcid.org/0000-0002-8726-2278"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]},{"id":"https://openalex.org/I36672615","display_name":"Courant Institute of Mathematical Sciences","ror":"https://ror.org/037tm7f56","country_code":"US","type":"education","lineage":["https://openalex.org/I36672615","https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Saporta, Adriel","raw_affiliation_strings":["Courant Institute New York University"],"affiliations":[{"raw_affiliation_string":"Courant Institute New York University","institution_ids":["https://openalex.org/I36672615","https://openalex.org/I57206974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022202456","display_name":"Rajesh Ranganath","orcid":null},"institutions":[{"id":"https://openalex.org/I36672615","display_name":"Courant Institute of Mathematical Sciences","ror":"https://ror.org/037tm7f56","country_code":"US","type":"education","lineage":["https://openalex.org/I36672615","https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ranganath, Rajesh","raw_affiliation_strings":["Courant Institute, Center for Data Science New York University"],"affiliations":[{"raw_affiliation_string":"Courant Institute, Center for Data Science New York University","institution_ids":["https://openalex.org/I36672615"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5007411646"],"corresponding_institution_ids":["https://openalex.org/I36672615"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"206","issue":null,"first_page":"8925","last_page":"8953"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","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/T13702","display_name":"Machine Learning in Healthcare","score":0.9962999820709229,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9850000143051147,"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/class","display_name":"Class (philosophy)","score":0.676234781742096},{"id":"https://openalex.org/keywords/attribution","display_name":"Attribution","score":0.6343519687652588},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.625354528427124},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5903846621513367},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4695081412792206},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.44048383831977844},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.44017690420150757},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.43530339002609253},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40931373834609985},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39716023206710815},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.395111620426178},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25218361616134644},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.10916030406951904},{"id":"https://openalex.org/keywords/social-psychology","display_name":"Social psychology","score":0.06829729676246643}],"concepts":[{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.676234781742096},{"id":"https://openalex.org/C143299363","wikidata":"https://www.wikidata.org/wiki/Q900584","display_name":"Attribution","level":2,"score":0.6343519687652588},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.625354528427124},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5903846621513367},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4695081412792206},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.44048383831977844},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.44017690420150757},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.43530339002609253},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40931373834609985},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39716023206710815},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.395111620426178},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25218361616134644},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.10916030406951904},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.06829729676246643},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmid:40290784","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40290784","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of machine learning research","raw_type":null},{"id":"pmh:oai:arXiv.org:2302.12893","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.12893","pdf_url":"https://arxiv.org/pdf/2302.12893","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2302.12893","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2302.12893","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2302.12893","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.12893","pdf_url":"https://arxiv.org/pdf/2302.12893","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1171700966","display_name":null,"funder_award_id":"NSF CAREER","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2109521444","display_name":null,"funder_award_id":"T32GM136573","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G3298056745","display_name":null,"funder_award_id":"R01HL","funder_id":"https://openalex.org/F4320337338","funder_display_name":"National Heart, Lung, and Blood Institute"},{"id":"https://openalex.org/G3530239307","display_name":null,"funder_award_id":"NHLBI","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G3683089223","display_name":null,"funder_award_id":"GM007308","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G5318077853","display_name":null,"funder_award_id":"GM136573","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G5693414920","display_name":null,"funder_award_id":"1922658","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5864292535","display_name":null,"funder_award_id":"T32GM007308","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G6275702937","display_name":null,"funder_award_id":"R01HL148248","funder_id":"https://openalex.org/F4320337338","funder_display_name":"National Heart, Lung, and Blood Institute"},{"id":"https://openalex.org/G6671297155","display_name":null,"funder_award_id":"CAREER","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7145278554","display_name":null,"funder_award_id":"T32GM136573","funder_id":"https://openalex.org/F4320337338","funder_display_name":"National Heart, Lung, and Blood Institute"},{"id":"https://openalex.org/G7445402995","display_name":null,"funder_award_id":"NHLBI,","funder_id":"https://openalex.org/F4320337338","funder_display_name":"National Heart, Lung, and Blood Institute"},{"id":"https://openalex.org/G7472439484","display_name":"Career: Building Models that Avoid Spurious Correlations through Interpretability and Representation Learning","funder_award_id":"2145542","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7643161287","display_name":null,"funder_award_id":"NIH/NHLBI","funder_id":"https://openalex.org/F4320337338","funder_display_name":"National Heart, Lung, and Blood Institute"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320317153","display_name":"DeepMind","ror":"https://ror.org/00971b260"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337338","display_name":"National Heart, Lung, and Blood Institute","ror":"https://ror.org/012pb6c26"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4322716343.pdf","grobid_xml":"https://content.openalex.org/works/W4322716343.grobid-xml"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W1787224781","https://openalex.org/W1849277567","https://openalex.org/W2194775991","https://openalex.org/W2240067561","https://openalex.org/W2516809705","https://openalex.org/W2594633041","https://openalex.org/W2605409611","https://openalex.org/W2902644322","https://openalex.org/W2912802472","https://openalex.org/W2914124878","https://openalex.org/W2925863688","https://openalex.org/W2937845937","https://openalex.org/W2950278227","https://openalex.org/W2953073956","https://openalex.org/W2962843949","https://openalex.org/W2962858109","https://openalex.org/W2962862931","https://openalex.org/W2963365341","https://openalex.org/W2963446712","https://openalex.org/W2963521553","https://openalex.org/W2964045325","https://openalex.org/W2970447476","https://openalex.org/W2972854536","https://openalex.org/W2979200397","https://openalex.org/W2980282514","https://openalex.org/W3004315562","https://openalex.org/W3006064320","https://openalex.org/W3009460750","https://openalex.org/W3027572331","https://openalex.org/W3101609372","https://openalex.org/W3107600318","https://openalex.org/W3118608800","https://openalex.org/W3124464611","https://openalex.org/W3157793490","https://openalex.org/W3204324161","https://openalex.org/W4282968708","https://openalex.org/W4287075876","https://openalex.org/W4287285866","https://openalex.org/W4288088628","https://openalex.org/W4293861706","https://openalex.org/W4300235091","https://openalex.org/W4320855262"],"related_works":["https://openalex.org/W2035546108","https://openalex.org/W2376361520","https://openalex.org/W2133328864","https://openalex.org/W2093949997","https://openalex.org/W2570200690","https://openalex.org/W2389726244","https://openalex.org/W3030478661","https://openalex.org/W2323536476","https://openalex.org/W2104624653","https://openalex.org/W2128730003"],"abstract_inverted_index":{"Feature":[0],"attribution":[1,17,34],"methods":[2,18,27,48,117,134],"identify":[3],"which":[4,91],"features":[5,105],"of":[6,39,72,106,128,139],"an":[7,66,78],"input":[8],"most":[9],"influence":[10],"a":[11,32,37,82,125],"model's":[12],"output.":[13],"Most":[14],"widely-used":[15],"feature":[16,33],"(such":[19],"as":[20,36],"SHAP,":[21],"LIME,":[22],"and":[23,112,131,146],"Grad-CAM)":[24],"are":[25],"\"class-dependent\"":[26],"in":[28],"that":[29,46,57,94,118],"they":[30],"generate":[31],"vector":[35],"function":[38],"class.":[40],"In":[41,85],"this":[42],"work,":[43],"we":[44,87,123],"demonstrate":[45],"class-dependent":[47,83,130],"can":[49],"\"leak\"":[50],"information":[51],"about":[52],"the":[53,70,96,107],"selected":[54],"class,":[55],"making":[56],"class":[58],"appear":[59],"more":[60],"likely":[61],"than":[62],"it":[63],"is.":[64],"Thus,":[65],"end":[67],"user":[68],"runs":[69],"risk":[71],"drawing":[73],"false":[74],"conclusions":[75],"when":[76],"interpreting":[77],"explanation":[79],"generated":[80],"by":[81],"method.":[84],"contrast,":[86],"introduce":[88,110],"\"distribution-aware\"":[89],"methods,":[90],"favor":[92],"explanations":[93],"keep":[95],"label's":[97],"distribution":[98,102],"close":[99],"to":[100],"its":[101],"given":[103],"all":[104],"input.":[108],"We":[109],"SHAP-KL":[111],"FastSHAP-KL,":[113],"two":[114],"baseline":[115],"distribution-aware":[116,133],"compute":[119],"Shapley":[120],"values.":[121],"Finally,":[122],"perform":[124],"comprehensive":[126],"evaluation":[127],"seven":[129],"three":[132,136],"on":[135],"clinical":[137],"datasets":[138],"different":[140],"high-dimensional":[141],"data":[142],"types:":[143],"images,":[144],"biosignals,":[145],"text.":[147]},"counts_by_year":[],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2023-03-03T00:00:00"}
