{"links":{},"id":212896,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00212896","sets":["1:10354:10373"]},"path":["10373"],"owner":"44499","recid":"212896","title":["5分で分かる!? 有名論文ナナメ読み:Marco T. Ribeiro et al.:“ Why Should I Trust You?”:Explaining the Predictions of Any Classifier"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-09-15"},"_buckets":{"deposit":"5aac1ab2-3cda-4d5b-94bf-7f30ad19c159"},"_deposit":{"id":"212896","pid":{"type":"depid","value":"212896","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"5分で分かる!? 有名論文ナナメ読み:Marco T. Ribeiro et al.:“ Why Should I Trust You?”:Explaining the Predictions of Any Classifier","author_link":["543863"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"5分で分かる!? 有名論文ナナメ読み:Marco T. Ribeiro et al.:“ Why Should I Trust You?”:Explaining the Predictions of Any Classifier"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"連載","subitem_subject_scheme":"Other"}]},"item_type_id":"30","publish_date":"2021-09-15","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_30_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"公立諏訪東京理科大学"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/212896/files/IPSJ-MGN621011.pdf","label":"IPSJ-MGN621011.pdf"},"date":[{"dateType":"Available","dateValue":"2023-09-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MGN621011.pdf","filesize":[{"value":"1.4 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"101edb47-e00e-4f73-b690-f60643169452","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_30_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"石井, 一夫"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"article"}]},"item_30_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116625","subitem_source_identifier_type":"NCID"}]},"item_30_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本稿では,解釈可能なAI(Explainable AI)のもっとも初期の論文であるLIMEを取り上げ,その特徴と限界,データ分析にどのように活用していくかについて論じる.深層学習で得られた学習モデルについて,なぜそのようなモデルが得られたのかは,通常説明が困難である.これを,解釈可能な線形回帰モデルや,決定木などに近似的に置き換えて,解釈性を持たせようとした試みが,explainable AIである.もともと,説明困難なものを近似的に解釈できるものに置き換えたものなので,どこまで,それが意義あるものかは,議論がある.しかし,これをいかにデータ分析に活用していくかという姿勢が重要である.","subitem_description_type":"Other"}]},"item_30_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"570","bibliographic_titles":[{"bibliographic_title":"情報処理"}],"bibliographicPageStart":"568","bibliographicIssueDates":{"bibliographicIssueDate":"2021-09-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"10","bibliographicVolumeNumber":"62"}]},"relation_version_is_last":true,"item_30_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.20729/00212790","subitem_identifier_reg_type":"JaLC"}]},"weko_creator_id":"44499"},"created":"2025-01-19T01:13:49.261751+00:00","updated":"2025-01-19T17:20:39.917095+00:00"}