{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T12:04:18Z","timestamp":1767182658951,"version":"3.28.0"},"reference-count":34,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T00:00:00Z","timestamp":1633046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T00:00:00Z","timestamp":1633046400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T00:00:00Z","timestamp":1633046400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10]]},"DOI":"10.1109\/vis49827.2021.9623271","type":"proceedings-article","created":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T19:10:43Z","timestamp":1638299443000},"page":"31-35","source":"Crossref","is-referenced-by-count":20,"title":["AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation"],"prefix":"10.1109","author":[{"given":"Oscar","family":"Gomez","sequence":"first","affiliation":[]},{"given":"Steffen","family":"Holter","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Enrico","family":"Bertini","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref33","first-page":"841","article-title":"Counterfactual explanations without opening the black box: Automated decisions and the gpdr","volume":"31","author":"wachter","year":"2017","journal-title":"Harv JL & Tech"},{"doi-asserted-by":"publisher","key":"ref32","DOI":"10.1145\/3287560.3287566"},{"doi-asserted-by":"publisher","key":"ref31","DOI":"10.1145\/3077257.3077260"},{"doi-asserted-by":"publisher","key":"ref30","DOI":"10.1109\/TVCG.2018.2865044"},{"doi-asserted-by":"publisher","key":"ref34","DOI":"10.1109\/TVCG.2019.2934619"},{"key":"ref10","article-title":"Local rule-based explanations of black box decision systems","author":"guidotti","year":"2018","journal-title":"arXiv preprint arXiv 1805 10820"},{"doi-asserted-by":"publisher","key":"ref11","DOI":"10.1145\/3236009"},{"doi-asserted-by":"publisher","key":"ref12","DOI":"10.1126\/scirobotics.aay7120"},{"doi-asserted-by":"publisher","key":"ref13","DOI":"10.1145\/3392878"},{"key":"ref14","article-title":"Heart disease data set","author":"janosi","year":"1988","journal-title":"The UCI KDD Archive"},{"doi-asserted-by":"publisher","key":"ref15","DOI":"10.1109\/VAST.2017.8585720"},{"doi-asserted-by":"publisher","key":"ref16","DOI":"10.1145\/3313831.3376590"},{"key":"ref17","article-title":"The mythos of model interpretability","author":"lipton","year":"2016","journal-title":"arXiv preprint arXiv 1606 03490"},{"doi-asserted-by":"publisher","key":"ref18","DOI":"10.1145\/3236386.3241340"},{"key":"ref19","first-page":"4765","article-title":"A unified approach to interpreting model predictions","author":"lundberg","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref28","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v32i1.11491","article-title":"Anchors: High-precision model-agnostic explanations","author":"ribeiro","year":"2018","journal-title":"Thirty-Second AAAI Conference on Artificial Intelligence"},{"key":"ref4","article-title":"Dece: Decision explorer with counterfactual explanations for machine learning models","author":"cheng","year":"2020","journal-title":"arXiv preprint arXiv 2008 06439"},{"doi-asserted-by":"publisher","key":"ref27","DOI":"10.1145\/2939672.2939778"},{"doi-asserted-by":"publisher","key":"ref3","DOI":"10.3390\/electronics8080832"},{"key":"ref6","article-title":"What does explainable ai really mean? a new conceptualization of perspectives","author":"doran","year":"2017","journal-title":"arXiv preprint arXiv 1710 00794"},{"key":"ref29","first-page":"4","article-title":"Towards extracting faithful and descriptive representations of latent variable models","volume":"1","author":"sanchez","year":"2015","journal-title":"AAAI Spring Syposium on Knowledge Representation and Reasoning (KRR) Integrating Symbolic and Neural Approaches"},{"key":"ref5","first-page":"24","article-title":"Extracting tree-structured representations of trained networks","author":"craven","year":"1996","journal-title":"Advances in neural information processing systems"},{"year":"2018","article-title":"Explainable machine learning challenge","key":"ref8"},{"key":"ref7","article-title":"Towards a rigorous science of interpretable machine learning","author":"doshi-velez","year":"2017","journal-title":"arXiv preprint arXiv 1702 08608"},{"key":"ref2","article-title":"Explanation and justification in machine learning: A survey","volume":"8","author":"biran","year":"2017","journal-title":"IJCAI-17 Workshop on Explainable AI"},{"doi-asserted-by":"publisher","key":"ref9","DOI":"10.1145\/3377325.3377536"},{"doi-asserted-by":"publisher","key":"ref1","DOI":"10.1109\/ACCESS.2018.2870052"},{"key":"ref20","article-title":"Preserving causal constraints in counterfactual explanations for machine learning classifiers","author":"mahajan","year":"2019","journal-title":"arXiv preprint arXiv 1912 03277"},{"doi-asserted-by":"publisher","key":"ref22","DOI":"10.1016\/j.artint.2018.07.007"},{"year":"2013","author":"martens","article-title":"Explaining data-driven document classifications","key":"ref21"},{"year":"2019","author":"molnar","article-title":"Interpretable Machine Learning","key":"ref24"},{"key":"ref23","article-title":"Explainable AI: Beware of inmates running the asylum or: How I learnt to stop worrying and love the social and behavioural sciences","author":"miller","year":"2017","journal-title":"IJCAI Workshop on Explainable Artificial Intelligence (X-AI)"},{"key":"ref26","article-title":"Manipulating and measuring model interpretability","author":"poursabzi-sangdeh","year":"2018","journal-title":"arXiv preprint arXiv 1802 07814"},{"doi-asserted-by":"publisher","key":"ref25","DOI":"10.1145\/3351095.3372850"}],"event":{"name":"2021 IEEE Visualization Conference (VIS)","start":{"date-parts":[[2021,10,24]]},"location":"New Orleans, LA, USA","end":{"date-parts":[[2021,10,29]]}},"container-title":["2021 IEEE Visualization Conference (VIS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9622895\/9623263\/09623271.pdf?arnumber=9623271","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T15:05:05Z","timestamp":1673881505000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9623271\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/vis49827.2021.9623271","relation":{},"subject":[],"published":{"date-parts":[[2021,10]]}}}