{"id":55419,"date":"2021-07-28T00:00:00","date_gmt":"2021-07-28T00:00:00","guid":{"rendered":"http:\/\/35.208.6.27\/xlscout_new\/explainable-ai\/"},"modified":"2024-10-16T09:57:17","modified_gmt":"2024-10-16T09:57:17","slug":"explainable-ai","status":"publish","type":"post","link":"https:\/\/xlscout.ai\/zh-hant\/explainable-ai\/","title":{"rendered":"What is Explainable AI?"},"content":{"rendered":"<p><span style=\"font-weight: 400;\"><a href=\"https:\/\/en.wikipedia.org\/wiki\/Explainable_artificial_intelligence\">Explainable Artificial Intelligence<\/a>\/ Explainable AI is key for any successful implementation at scale.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Here are some of the aspects:\u00a0<\/span><\/p>\n<h2><strong>Explanation of Explainable AI<\/strong><\/h2>\n<p>Systems provide the evidence or reason(s) for all outputs.<br \/>\n<b><\/b><\/p>\n<h2><strong>Meaningful<\/strong><\/h2>\n<p>Systems provide explanations that are understandable to individual users.<br \/>\n<b><\/b><\/p>\n<h2><strong>Explanation Accuracy<\/strong><\/h2>\n<p>The explanation correctly reflects the system\u2019s process for generating the output.<br \/>\n<b><\/b><\/p>\n<h2><strong>Knowledge Limits<\/strong><b><\/b><\/h2>\n<p style=\"color: #252525;\">The system only operates under the conditions for which it was designed or when it reaches sufficient confidence in its output.<\/p>\n<p style=\"color: #252525;\">IP analysis has always been considered a great use case for AI systems because of the multiple factors involved, such as rich, large text data and several interconnections.<\/p>\n<p><span style=\"font-weight: 400;\">However, for large scale adoption in Industry the following are needed:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Transparency\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><a href=\"https:\/\/xlscout.ai\/taxonomy-bot-are-they-for-real\/\">Explainability<\/a>\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Lack of bias\u00a0<\/span><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Explainable Artificial Intelligence\/ Explainable AI is &#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[189],"tags":[190],"class_list":["post-55419","post","type-post","status-publish","format-standard","hentry","category-blog","tag-general","no-post-thumbnail"],"acf":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/xlscout.ai\/zh-hant\/wp-json\/wp\/v2\/posts\/55419","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/xlscout.ai\/zh-hant\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/xlscout.ai\/zh-hant\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/xlscout.ai\/zh-hant\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/xlscout.ai\/zh-hant\/wp-json\/wp\/v2\/comments?post=55419"}],"version-history":[{"count":0,"href":"https:\/\/xlscout.ai\/zh-hant\/wp-json\/wp\/v2\/posts\/55419\/revisions"}],"wp:attachment":[{"href":"https:\/\/xlscout.ai\/zh-hant\/wp-json\/wp\/v2\/media?parent=55419"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/xlscout.ai\/zh-hant\/wp-json\/wp\/v2\/categories?post=55419"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/xlscout.ai\/zh-hant\/wp-json\/wp\/v2\/tags?post=55419"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}