{"id":707,"date":"2021-05-13T20:13:20","date_gmt":"2021-05-13T20:13:20","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/haxtoolkit\/?post_type=pattern&#038;p=707"},"modified":"2023-10-25T07:50:31","modified_gmt":"2023-10-25T14:50:31","slug":"g11-f-example-based-explanations","status":"publish","type":"pattern","link":"https:\/\/www.microsoft.com\/en-us\/haxtoolkit\/pattern\/g11-f-example-based-explanations\/","title":{"rendered":"Pattern 11F: Example-based explanations"},"content":{"rendered":"\n\n<div class=\"toolkit-heading-with-image-block\">\n\t\n\n<h2 class=\"wp-block-heading\">Pattern 11F: Example-based explanations<\/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\/yellow-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 user needs an explanation for why the system did what it did and an explanation for a single instance may not be sufficient.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"solution\">Solution<\/h4>\n\n\n\n<p>Provide one or multiple examples of instances similar to the user\u2019s input and their corresponding explanations.<\/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>It is more efficient for the user to understand the AI system\u2019s behavior from examples.<\/li>\n\n\n\n<li>A full explanation is too complex to communicate efficiently.<\/li>\n\n\n\n<li>Often useful for systems dealing with images, audio, or video.<\/li>\n\n\n\n<li>The user doesn&#8217;t understand why the system took a specific action.<\/li>\n\n\n\n<li>The user wants to understand the system\u2019s logic.<\/li>\n\n\n\n<li>Policy or regulations require the system to make an explanation available.<\/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 collect information about how similar examples to the user\u2019s input can be extracted from the system\u2019s past by:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Defining criteria for similarity.<\/li>\n\n\n\n<li>Retrieving top examples similar to the user\u2019s input, based on similarity criteria.<\/li>\n<\/ul>\n\n\n\n<p>When selecting what examples to display, consider:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Which dimension(s) of similarity between examples and user input to illustrate.<\/li>\n\n\n\n<li>Including diverse examples to illustrate multiple dimensions of similarity, avoiding redundancy.<\/li>\n<\/ul>\n\n\n\n<p>Provide an initial set of examples and enable the user to access additional ones on demand.<\/p>\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>Facilitates user understanding by showing rather than telling.<\/li>\n\n\n\n<li>Enables the user to understand specific AI system decisions.<\/li>\n\n\n\n<li>Builds user trust in the system.<\/li>\n\n\n\n<li>Enables the user to quickly understand the system\u2019s reasoning.<\/li>\n\n\n\n<li>Understanding the system\u2019s reasoning in turn enables the user to predict the system\u2019s behavior and troubleshoot when the system\u2019s behavior is undesirable.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"common-pitfalls\">Common pitfalls<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Criteria for similarity are not meaningful to users.Providing not enough or too many examples for the user\u2019s context.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"references\">References<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Saleema Amershi, James Fogarty, Ashish Kapoor, and Desney Tan. 2009.&nbsp;<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/overview-based-example-selection-end-user-interactive-concept-learning\/#:~:text=Overview-Based%20Example%20Selection%20in%20End-User%20Interactive%20Concept%20Learning.%2cconcepts%2c%20a%20strategy%20known%20as%20interactive%20concept%20learning.\" target=\"_blank\" rel=\"noreferrer noopener\">Overview based example selection in end user interactive concept learning<\/a>. In&nbsp;<em>Proceedings of the 22nd annual ACM symposium on User interface software and technology<\/em>&nbsp;(<em>UIST &#8217;09<\/em>). Association for Computing Machinery, New York, NY, USA, 247\u2013256. DOI:<a href=\"https:\/\/doi.org\/10.1145\/1622176.1622222\">https:\/\/doi.org\/10.1145\/1622176.1622222<\/a><\/li>\n\n\n\n<li>Carrie J. Cai, Jonas Jongejan, and Jess Holbrook. 2019.&nbsp;<a href=\"https:\/\/dl.acm.org\/doi\/10.1145\/3301275.3302289\" target=\"_blank\" rel=\"noreferrer noopener\">The effects of example-based explanations in a machine learning interface<\/a>. In&nbsp;<em>Proceedings of the 24th International Conference on Intelligent User Interfaces<\/em>&nbsp;(<em>IUI &#8217;19<\/em>). Association for Computing Machinery, New York, NY, USA, 258\u2013262. DOI:https:\/\/doi.org\/10.1145\/3301275.3302289<\/li>\n<\/ul>\n","protected":false},"menu_order":116,"template":"","meta":{"toolkit_pattern_number":"11F","ep_exclude_from_search":false},"application-type":[68,61,57,96,60,99,56,55,69,29,58,30,59],"goal":[114,126,123],"guideline-term":[13],"product-category":[71,70,23,64,62,26,66,28,65,31,32,63],"class_list":["post-707","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-appropriate-reliance","goal-reliability","goal-transparency","guideline-term-make-clear-why-the-system-did-what-it-did","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 11F: Example-based explanations - 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\/g11-f-example-based-explanations\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pattern 11F: Example-based explanations - Microsoft HAX Toolkit\" \/>\n<meta property=\"og:description\" content=\"Problem The user needs an explanation for why the system did what it did and an explanation for a single instance may not be sufficient. 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