{"id":713,"date":"2021-05-13T20:16:11","date_gmt":"2021-05-13T20:16:11","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/haxtoolkit\/?post_type=pattern&#038;p=713"},"modified":"2023-10-25T07:39:56","modified_gmt":"2023-10-25T14:39:56","slug":"g14-b-immediate-partial-non-disruptive-updates","status":"publish","type":"pattern","link":"https:\/\/www.microsoft.com\/en-us\/haxtoolkit\/pattern\/g14-b-immediate-partial-non-disruptive-updates\/","title":{"rendered":"Pattern 14B: Immediate, partial, non-disruptive updates"},"content":{"rendered":"\n\n<div class=\"toolkit-heading-with-image-block\">\n\t\n\n<h2 class=\"wp-block-heading\">Pattern 14B: Immediate, partial, non-disruptive updates<\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" src=\"https:\/\/www.microsoft.com\/en-us\/haxtoolkit\/uploads\/prod\/2023\/05\/green-header-bar-1536x14-5-23.png\" alt=\"\" \/><\/figure>\n\n<\/div>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"problem\">Problem<\/h4>\n\n\n\n<p>The system adapts in response to user interaction and is at risk of disrupting or disorienting the user.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"solution\">Solution<\/h4>\n\n\n\n<p>The system makes an immediate, but local update that maintains, to a large extent, the previous state.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"use-when\">Use when<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The model is capable of accepting additional information from user interactions, interpreting user intent, and adapting over time.<\/li>\n\n\n\n<li>Delaying the update would prevent the user from accomplishing their goals efficiently.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"how\">How<\/h4>\n\n\n\n<p>Collaborate with an AI\/ML practitioner to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Decide what user interactions to respond to. Identify which user interactions convey additional information about the user or their intent.<\/li>\n\n\n\n<li>Use the additional information to get an updated prediction or result set.<\/li>\n<\/ul>\n\n\n\n<p>When making the update, consider:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Adjusting the outputs by changing only a small part.<\/li>\n\n\n\n<li>Placing the updated items in the context of existing ones, at the periphery of the user\u2019s attention so as not to be disruptive. Consider employing a subtle attention-drawing mechanism to help the user notice the update and overcome change blindness.<\/li>\n\n\n\n<li>Triggering the change from the user interaction.<\/li>\n\n\n\n<li>Displaying all the adjusted outputs at once.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"user-benefits\">User benefits<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Provides value to users by improving relevance of content.<\/li>\n\n\n\n<li>Enables the user to discover new relevant content.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"common-pitfalls\">Common pitfalls<\/h4>\n\n\n\n<p>Consult with AI\/ML practitioners on your team to guard against these pitfalls:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The model is wrong or uncertain about which outputs need to be changed.<\/li>\n\n\n\n<li>The update may introduce new errors accidentally.<\/li>\n\n\n\n<li>The model update may have cascading effects and change other outputs unintentionally.<\/li>\n<\/ul>\n\n\n\n<p>See<a href=\"https:\/\/www.microsoft.com\/en-us\/haxtoolkit\/guideline\/support-efficient-correction\/\" target=\"_blank\" rel=\"noreferrer noopener\">&nbsp;Guideline 9,<\/a><em><a href=\"https:\/\/docs.microsoft.com\/en-us\/ai\/guidelines-human-ai-interaction\/guideline-09\" target=\"_blank\" rel=\"noreferrer noopener\">&nbsp;Support efficient correction<\/a><\/em>,&nbsp;<a href=\"https:\/\/www.microsoft.com\/en-us\/haxtoolkit\/guideline\/scope-services-when-in-doubt\/\" target=\"_blank\" rel=\"noreferrer noopener\">Guideline 10,&nbsp;<em>Scope services when in doubt<\/em><\/a>, and<a href=\"https:\/\/www.microsoft.com\/en-us\/haxtoolkit\/guideline\/make-clear-why-the-system-did-what-it-did\/\" target=\"_blank\" rel=\"noreferrer noopener\">&nbsp;Guideline 11,&nbsp;<em>Make clear why the system did what it did<\/em>,&nbsp;<\/a>and their respective patterns for what to do when the system is wrong.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"references\">References<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Jin Young Kim, Mark Cramer, Jaime Teevan, and Dmitry Lagun. 2013.<a href=\"http:\/\/teevan.org\/publications\/papers\/cikm13.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">&nbsp;Understanding how people interact with web search results that change in real-time using implicit feedback<\/a>. In&nbsp;<em>Proceedings of the 22nd ACM international conference on Information &amp;&nbsp;Knowledge Management<\/em>&nbsp;(<em>CIKM &#8217;13<\/em>). Association for Computing Machinery, New York, NY, USA, 2321\u20132326. DOI:https:\/\/doi.org\/10.1145\/2505515.2505663<\/li>\n<\/ul>\n","protected":false},"menu_order":142,"template":"","meta":{"toolkit_pattern_number":"14B","ep_exclude_from_search":false},"application-type":[68,61,57,96,60,99,56,55,69,29,58,30,59],"goal":[120,126],"guideline-term":[16],"product-category":[71,70,23,64,62,26,66,28,65,31,32,63],"class_list":["post-713","pattern","type-pattern","status-publish","hentry","application-type-classification","application-type-facial-recognition","application-type-filtering-and-ranking","application-type-gen-ai","application-type-image-recognition","application-type-llm","application-type-natural-language-processing-text","application-type-natural-language-processing-voice","application-type-prediction","application-type-recommendation","application-type-route-planning","application-type-search","application-type-text-generation","goal-personalization","goal-reliability","guideline-term-update-and-adapt-cautiously","product-category-advertising","product-category-chatbot","product-category-e-commerce","product-category-email","product-category-health-and-wellness","product-category-navigation","product-category-media-entertainment","product-category-productivity","product-category-search-engine","product-category-social-networks","product-category-voice-assistants","product-category-writing-and-editing"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Pattern 14B: Immediate, partial, non-disruptive updates - Microsoft HAX Toolkit<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.microsoft.com\/en-us\/haxtoolkit\/pattern\/g14-b-immediate-partial-non-disruptive-updates\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pattern 14B: Immediate, partial, non-disruptive updates - Microsoft HAX Toolkit\" \/>\n<meta property=\"og:description\" content=\"Problem The system adapts in response to user interaction and is at risk of disrupting or disorienting the user. 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