{"id":"https://openalex.org/W2965593338","doi":null,"title":"Contrastive Explanations for Large Errors in Retail Forecasting Predictions through Monte Carlo Simulations","display_name":"Contrastive Explanations for Large Errors in Retail Forecasting Predictions through Monte Carlo Simulations","publication_year":2019,"publication_date":"2019-08-11","ids":{"openalex":"https://openalex.org/W2965593338","mag":"2965593338"},"language":"en","primary_location":{"id":"mag:2965593338","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1908.00085.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1908.00085.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005269119","display_name":"Ante Luci\u0107","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ante Luci\u0107","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026579864","display_name":"Hinda Haned","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hinda Haned","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5031439294","display_name":"Maarten de Rijke","orcid":"https://orcid.org/0000-0002-1086-0202","observed_orcids":["https://orcid.org/0000-0002-1086-0202"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maarten de Rijke","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-1086-0202","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":3,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":1.0,"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":1.0,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10743","display_name":"Software Testing and Debugging Techniques","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1712","display_name":"Software"},"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/contrastive-explanations","display_name":"contrastive explanations","score":0.9340000152587891},{"id":"https://openalex.org/keywords/sales-forecasting","display_name":"sales forecasting","score":0.6880000233650208},{"id":"https://openalex.org/keywords/retail-forecasting","display_name":"retail forecasting","score":0.7730000019073486},{"id":"https://openalex.org/keywords/explainable-machine-learning","display_name":"explainable machine learning","score":0.7260000109672546},{"id":"https://openalex.org/keywords/black-box-modeling","display_name":"black-box models","score":0.9110000133514404},{"id":"https://openalex.org/keywords/monte-carlo-simulation","display_name":"Monte Carlo simulation","score":0.8410000205039978},{"id":"https://openalex.org/keywords/feature-perturbation","display_name":"feature perturbation","score":0.5249999761581421},{"id":"https://openalex.org/keywords/user-study","display_name":"user study","score":0.7300000190734863}],"concepts":[{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.7330939173698425},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7005985975265503},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6533344984054565},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4505062401294708},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44331586360931396},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3419356346130371},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3301817774772644},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18006131052970886},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13630908727645874},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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":3,"locations":[{"id":"mag:2965593338","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1908.00085.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:dare.uva.nl:openaire/ac7ea157-bfcd-44e1-b930-bbc0a97762ad","doi":null,"is_oa":true,"landing_page_url":"https://handle.uba.uva.nl/personal/pure/en/publications/contrastive-explanations-for-large-errors-in-retail-forecasting-predictions-through-monte-carlo-simulations(ac7ea157-bfcd-44e1-b930-bbc0a97762ad).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400088","display_name":"UvA-DARE (University of Amsterdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I887064364","host_organization_name":"University of Amsterdam","host_organization_lineage":["https://openalex.org/I887064364"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Lucic, A, Haned, H & de Rijke, M 2019, Contrastive Explanations for Large Errors in Retail Forecasting Predictions through Monte Carlo Simulations. in T Miller, R Weber & D Magazzeni (eds), Proceedings of the IJCAI 2019 Workshop on Explainable Artificial Intelligence. pp. 66-72, IJCAI 2019 Workshop on Explainable Artificial Intelligence, Macau, China, 11/08/19. < https://arxiv.org/abs/1908.00085v1 >","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:uvapub:oai:dare.uva.nl:publications/ac7ea157-bfcd-44e1-b930-bbc0a97762ad","doi":null,"is_oa":true,"landing_page_url":"https://dare.uva.nl/personal/pure/en/publications/contrastive-explanations-for-large-errors-in-retail-forecasting-predictions-through-monte-carlo-simulations(ac7ea157-bfcd-44e1-b930-bbc0a97762ad).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proceedings of the IJCAI 2019 Workshop on Explainable Artificial Intelligence, 66 - 72","raw_type":"info:eu-repo/semantics/conferencepaper"}],"best_oa_location":{"id":"mag:2965593338","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1908.00085.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G3032427513","display_name":"NEWEL - The New Science of Existential Well-being","funder_award_id":"652.001.003","funder_id":"https://openalex.org/F4320321800","funder_display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek"}],"funders":[{"id":"https://openalex.org/F4320321182","display_name":"VSNU Vereniging van Universiteiten","ror":"https://ror.org/05wvdt748"},{"id":"https://openalex.org/F4320321800","display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","ror":"https://ror.org/04jsz6e67"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2989620457","https://openalex.org/W4288083796","https://openalex.org/W6990801905","https://openalex.org/W2824228311","https://openalex.org/W7077150439","https://openalex.org/W7139960362","https://openalex.org/W2889209799","https://openalex.org/W3043865406","https://openalex.org/W7009904602","https://openalex.org/W2941874280"],"abstract_inverted_index":{"At":[0],"Ahold":[1,145],"Delhaize,":[2],"there":[3],"is":[4,18,75,119],"an":[5,76,176],"interest":[6],"in":[7,46,97,166,175,220,226],"using":[8],"more":[9,223],"complex":[10],"machine":[11],"learning":[12],"techniques":[13,30],"for":[14,81],"sales":[15],"forecasting.":[16],"It":[17],"difficult":[19],"to":[20,27,36,50,152,186],"convince":[21],"analysts,":[22],"along":[23,124],"with":[24,125,141,194,200],"their":[25],"superiors,":[26],"adopt":[28],"these":[29,201],"since":[31],"the":[32,52,109,120,191,215,227],"models":[33,45],"are":[34,184],"considered":[35],"be'black":[37],"boxes,'even":[38],"if":[39,154,171],"they":[40],"perform":[41],"better":[42],"than":[43,224],"current":[44],"use.":[47],"We":[48,135,204],"aim":[49],"explore":[51],"impact":[53],"of":[54,122],"contrastive":[55,202],"explanations":[56,155,212],"about":[57,190],"large":[58,87,168,218],"errors":[59,219],"on":[60,112,137],"users'":[61],"attitudes":[62],"towards":[63],"a":[64,86,98,138,149,163,167],"'black-box'model.":[65],"In":[66],"this":[67,172],"work,":[68],"we":[69],"make":[70],"two":[71],"contributions.":[72],"The":[73,116,179],"first":[74],"algorithm,":[77],"Monte":[78,113],"Carlo":[79,114],"Bounds":[80],"Reasonable":[82],"Predictions":[83],"(MC-BRP).":[84],"Given":[85],"error,":[88,169],"MC-BRP":[89,123,158,211],"determines":[90],"(1)":[91],"feature":[92,107],"values":[93],"that":[94,182,207],"would":[95],"result":[96],"reasonable":[99],"prediction,":[100],"and":[101,108,132,170],"(2)":[102],"general":[103],"trends":[104],"between":[105],"each":[106],"target,":[110],"based":[111],"simulations.":[115],"second":[117],"contribution":[118],"evaluation":[121],"its":[126],"outcomes,":[127],"which":[128],"has":[129],"both":[130],"objective":[131,188],"subjective":[133],"components.":[134],"evaluate":[136],"real":[139,142],"dataset":[140],"users":[143,160,183,208,225],"from":[144],"Delhaize":[146],"by":[147,157],"conducting":[148],"user":[150],"study":[151,180],"determine":[153],"generated":[156],"help":[159],"understand":[161,213],"why":[162,214],"prediction":[164],"results":[165],"promotes":[173],"trust":[174],"automatically-learned":[177],"model.":[178],"shows":[181],"able":[185],"answer":[187],"questions":[189],"model's":[192],"predictions":[193,221],"overall":[195],"81.7%":[196],"accuracy":[197],"when":[198],"provided":[199],"explanations.":[203],"also":[205],"show":[206],"who":[209],"saw":[210],"model":[216],"makes":[217],"significantly":[222],"control":[228],"group.":[229]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2026-09-12T09:15:50.089291","created_date":"2019-08-13T00:00:00"}
