{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T11:00:04Z","timestamp":1760785204975,"version":"3.32.0"},"reference-count":6,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:p>Machine learning is routinely used to automate consequential decisions about users in domains such as finance and healthcare, raising concerns of transparency and recourse for negative outcomes. Existing Explainable AI techniques generate a static counterfactual point explanation which recommends changes to a user's instance to obtain a positive outcome. Unfortunately, these recommendations are often difficult or impossible for users to realistically enact. To overcome this, we present FACET, the first interactive robust explanation system which generates personalized counterfactual region explanations. FACET's expressive explanation analytics empower users to explore and compare multiple counterfactual options and develop a personalized actionable plan for obtaining their desired outcome. Visitors to the demonstration will interact with FACET via a new web dashboard for explanations of a loan approval scenario. In doing so, visitors will experience how lay users can easily leverage powerful explanation analytics through visual interactions and displays without the need for a strong technical background.<\/jats:p>","DOI":"10.14778\/3685800.3685872","type":"journal-article","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T17:25:21Z","timestamp":1731086721000},"page":"4349-4352","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Counterfactual Explanation Analytics: Empowering Lay Users to Take Action Against Consequential Automated Decisions"],"prefix":"10.14778","volume":"17","author":[{"given":"Peter M.","family":"VanNostrand","sequence":"first","affiliation":[{"name":"Worcester Polytechnic Institute"}]},{"given":"Dennis M.","family":"Hofmann","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute"}]},{"given":"Lei","family":"Ma","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute"}]},{"given":"Belisha","family":"Genin","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute"}]},{"given":"Randy","family":"Huang","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute"}]},{"given":"Elke. A.","family":"Rundensteiner","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute"}]}],"member":"320","published-online":{"date-parts":[[2024,11,8]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the Twenty-second International Conference on Very Large Data-Bases, T. M. Vijayaraman (Ed.). Morgan Kaufmann","author":"Berchtold Stefan","year":"1996","unstructured":"Stefan Berchtold, Daniel A. Keim, and Hans-Peter Kriegel. 1996. The X-Tree: An Index Structure for High-Dimensional Data. In Proceedings of the Twenty-second International Conference on Very Large Data-Bases, T. M. Vijayaraman (Ed.). Morgan Kaufmann, San Francisco, 28--39."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-022-00831-6"},{"key":"e_1_2_1_3_1","unstructured":"Kaggle. 2008. Loan Predication. kaggle.com\/datasets\/ninzaami\/loan-predication."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2909204"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3593013.3594001"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626729"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3685800.3685872","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T05:25:55Z","timestamp":1735622755000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3685800.3685872"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8]]},"references-count":6,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["10.14778\/3685800.3685872"],"URL":"https:\/\/doi.org\/10.14778\/3685800.3685872","relation":{},"ISSN":["2150-8097"],"issn-type":[{"type":"print","value":"2150-8097"}],"subject":[],"published":{"date-parts":[[2024,8]]},"assertion":[{"value":"2024-11-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}