{"id":"https://openalex.org/W4394867082","doi":"https://doi.org/10.48550/arxiv.2404.09601","title":"Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression","display_name":"Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression","publication_year":2024,"publication_date":"2024-04-15","ids":{"openalex":"https://openalex.org/W4394867082","doi":"https://doi.org/10.48550/arxiv.2404.09601"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2404.09601","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2404.09601","pdf_url":"https://arxiv.org/pdf/2404.09601","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2404.09601","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082253730","display_name":"Dilyara Bareeva","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bareeva, Dilyara","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061634795","display_name":"Maximilian Dreyer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dreyer, Maximilian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059724969","display_name":"Frederik Pahde","orcid":"https://orcid.org/0000-0002-5681-6231"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pahde, Frederik","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026451495","display_name":"Wojciech Samek","orcid":"https://orcid.org/0000-0002-6283-3265"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Samek, Wojciech","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5017608155","display_name":"Sebastian Lapuschkin","orcid":"https://orcid.org/0000-0002-0762-7258"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lapuschkin, Sebastian","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5082253730"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.911300003528595,"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":0.911300003528595,"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/harm","display_name":"Harm","score":0.7324804663658142},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.44948631525039673},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3685634732246399},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.35532212257385254},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.3343852162361145},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.24521204829216003},{"id":"https://openalex.org/keywords/social-psychology","display_name":"Social psychology","score":0.16996201872825623}],"concepts":[{"id":"https://openalex.org/C2777363581","wikidata":"https://www.wikidata.org/wiki/Q15098235","display_name":"Harm","level":2,"score":0.7324804663658142},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.44948631525039673},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3685634732246399},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.35532212257385254},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3343852162361145},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.24521204829216003},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.16996201872825623},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2404.09601","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2404.09601","pdf_url":"https://arxiv.org/pdf/2404.09601","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2404.09601","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2404.09601","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2404.09601","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2404.09601","pdf_url":"https://arxiv.org/pdf/2404.09601","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4394867082.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W4391913857","https://openalex.org/W2350741829","https://openalex.org/W2530322880"],"abstract_inverted_index":{"Deep":[0],"Neural":[1],"Networks":[2],"are":[3],"prone":[4],"to":[5,26,55,96],"learning":[6],"and":[7,83,131],"relying":[8],"on":[9,30,80,156],"spurious":[10,157],"correlations":[11],"in":[12,106,127],"the":[13,62,90,102,144,148],"training":[14],"data,":[15],"which,":[16],"for":[17,108],"high-risk":[18],"applications,":[19],"can":[20,37,47,93,142],"have":[21,33],"fatal":[22],"consequences.":[23],"Various":[24],"approaches":[25],"suppress":[27],"model":[28,57,72],"reliance":[29,155],"harmful":[31],"features":[32],"been":[34],"proposed":[35],"that":[36,139],"be":[38,48,94],"applied":[39,49,95,149],"post-hoc":[40,98],"without":[41],"additional":[42],"training.":[43],"Whereas":[44],"those":[45],"methods":[46],"with":[50,132],"efficiency,":[51],"they":[52],"also":[53],"tend":[54],"harm":[56],"performance":[58],"by":[59],"globally":[60],"shifting":[61],"distribution":[63],"of":[64,71,104,147],"latent":[65],"features.":[66,158],"To":[67],"mitigate":[68],"unintended":[69],"overcorrection":[70],"behavior,":[73],"we":[74,100,137],"propose":[75],"a":[76,115,133],"reactive":[77,91],"approach":[78,92],"conditioned":[79],"model-derived":[81],"knowledge":[82],"eXplainable":[84],"Artificial":[85],"Intelligence":[86],"(XAI)":[87],"insights.":[88],"While":[89],"many":[97],"methods,":[99],"demonstrate":[101],"incorporation":[103],"reactivity":[105,141],"particular":[107],"P-ClArC":[109],"(Projective":[110],"Class":[111,121],"Artifact":[112,122],"Compensation),":[113],"introducing":[114,140],"new":[116],"method":[117],"called":[118],"R-ClArC":[119],"(Reactive":[120],"Compensation).":[123],"Through":[124],"rigorous":[125],"experiments":[126],"controlled":[128],"settings":[129],"(FunnyBirds)":[130],"real-world":[134],"dataset":[135],"(ISIC2019),":[136],"show":[138],"minimize":[143],"detrimental":[145],"effect":[146],"correction":[150],"while":[151],"simultaneously":[152],"ensuring":[153],"low":[154]},"counts_by_year":[],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2024-04-17T00:00:00"}
