{"id":815857,"date":"2022-02-01T10:43:55","date_gmt":"2022-02-01T18:43:55","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?p=815857"},"modified":"2022-11-01T09:02:31","modified_gmt":"2022-11-01T16:02:31","slug":"advancing-ai-trustworthiness-updates-on-responsible-ai-research","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/advancing-ai-trustworthiness-updates-on-responsible-ai-research\/","title":{"rendered":"Advancing AI trustworthiness: Updates on responsible AI research"},"content":{"rendered":"\n<figure class=\"wp-block-image alignwide size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1401\" height=\"789\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog.jpg\" alt=\"blue graphic with a light honeycomb pattern background featuring a lightbulb in the middle and various icons around it: handshake, eye, connected people, balanced scale, lock, and shield\" class=\"wp-image-815860\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog.jpg 1401w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog-300x169.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog-1024x577.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog-768x433.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog-1066x600.jpg 1066w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog-655x368.jpg 655w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog-343x193.jpg 343w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog-240x135.jpg 240w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog-640x360.jpg 640w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog-960x540.jpg 960w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog-1280x720.jpg 1280w\" sizes=\"(max-width: 1401px) 100vw, 1401px\" \/><\/figure>\n\n\n\n<p><strong><em>Editor\u2019s note:<\/em><\/strong> <em>This year in review is a sampling of responsible AI research compiled by Aether, a Microsoft cross-company initiative on AI Ethics and Effects in Engineering and Research, as outreach from their commitment to advancing the practice of human-centered responsible AI. Although each paper includes authors who are participants in Aether, the research presented here expands beyond, encompassing work from across Microsoft, as well as with collaborators in academia and industry.<\/em>&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-embed aligncenter is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Making AI systems worthy of trust is critical for harnessing AI in valuable ways\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/FWvOyxZzIJw?feature=oembed&rel=0\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><figcaption>Chief Scientific Officer Eric Horvitz: Efforts to make AI systems worthy of trust are a critical part of building valuable AI applications<\/figcaption><\/figure>\n\n\n\n<p>Inflated expectations around the capabilities of AI technologies may lead people to believe that computers can\u2019t be wrong. The truth is AI failures are not a matter of <em>if <\/em>but <em>when.<\/em> AI is a human endeavor that combines information about people and the physical world into mathematical constructs. Such technologies typically rely on statistical methods, with the possibility for errors throughout an AI system\u2019s lifespan. As AI systems become more widely used across domains, especially in high-stakes scenarios where people\u2019s safety and wellbeing can be affected, a critical question must be addressed: how trustworthy are AI systems, and <em>how<\/em> <em>much <\/em>and <em>when<\/em> should people trust AI?&nbsp;<\/p>\n\n\n\n<div class=\"annotations \" data-bi-aN=\"margin-callout\">\n\t<ul class=\"annotations__list card depth-16 bg-body p-4 annotations__list--right\">\n\t\t<li class=\"annotations__list-item\">\n\t\t\t\t\t\t\t<a href=\"https:\/\/www.microsoft.com\/en-us\/ai\/responsible-ai-resources\" target=\"_self\" aria-label=\"Explore responsible AI resources\" data-bi-type=\"annotated-link\" data-bi-cN=\"Explore responsible AI resources\" class=\"annotations__list-thumbnail\" >\n\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"172\" height=\"96\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_RAI_resources_thumbnail-240x135.jpg\" class=\"mb-2\" alt=\"a. Responsible AI Resources \u2013 Microsoft AI home page\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_RAI_resources_thumbnail-240x135.jpg 240w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_RAI_resources_thumbnail-300x169.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_RAI_resources_thumbnail-1024x577.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_RAI_resources_thumbnail-768x433.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_RAI_resources_thumbnail-1066x600.jpg 1066w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_RAI_resources_thumbnail-655x368.jpg 655w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_RAI_resources_thumbnail-343x193.jpg 343w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_RAI_resources_thumbnail-640x360.jpg 640w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_RAI_resources_thumbnail-960x540.jpg 960w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_RAI_resources_thumbnail-1280x720.jpg 1280w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_RAI_resources_thumbnail.jpg 1401w\" sizes=\"(max-width: 172px) 100vw, 172px\" \/>\t\t\t\t<\/a>\n\t\t\t\t\t\t\t<span class=\"annotations__type d-block text-uppercase font-weight-semibold text-neutral-300 small\"><\/span>\n\t\t\t<a href=\"https:\/\/www.microsoft.com\/en-us\/ai\/responsible-ai-resources\" target=\"_self\" class=\"annotations__link font-weight-semibold text-decoration-none\" data-bi-type=\"annotated-link\" aria-label=\"Explore responsible AI resources\" data-bi-aN=\"margin-callout\" data-bi-cN=\"Explore responsible AI resources\">\n\t\t\t\tExplore responsible AI resources&nbsp;<span class=\"glyph-append glyph-append-share glyph-append-xsmall\"><\/span>\n\t\t\t<\/a>\n\t\t\t\t\t\t\t<p class=\"annotations__caption text-neutral-400 mt-2\">Designed to help you responsibly use AI at every stage of development.<\/p>\n\t\t\t\t\t<\/li>\n\t<\/ul>\n<\/div>\n\n\n\n<p>As part of their ongoing commitment to <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/blog\/research-collection-research-supporting-responsible-ai\/\">building AI responsibly<\/a>, research scientists and engineers at Microsoft are pursuing methods and technologies aimed at helping builders of AI systems cultivate <em>appropriate trust<\/em>\u2014that is, building trustworthy models with reliable behaviors and clear communication that set proper expectations. When AI builders plan for failures, work to understand the nature of the failures, and implement ways to effectively mitigate potential harms, they help engender trust that can lead to a greater realization of AI\u2019s benefits.&nbsp;<\/p>\n\n\n\n<p>Pursuing trustworthiness across AI systems captures the intent of multiple projects on the responsible development and fielding of AI technologies. Numerous efforts at Microsoft have been nurtured by its <a href=\"https:\/\/www.microsoft.com\/en-us\/ai\/our-approach?activetab=pivot1%3aprimaryr5\" target=\"_blank\" rel=\"noreferrer noopener\">Aether Committee<\/a>, a coordinative cross-company council comprised of working groups focused on technical leadership at the frontiers of innovation in responsible AI. The effort is led by researchers and engineers at Microsoft Research and from across the company and is chaired by <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/horvitz\/\">Chief Scientific Officer Eric Horvitz<\/a>. Beyond research, Aether has advised Microsoft leadership on responsible AI challenges and opportunities since the committee\u2019s inception in 2016.&nbsp;<\/p>\n\n\n\n<div class=\"annotations \" data-bi-aN=\"margin-callout\">\n\t<ul class=\"annotations__list card depth-16 bg-body p-4 annotations__list--left\">\n\t\t<li class=\"annotations__list-item\">\n\t\t\t\t\t\t\t<a href=\"https:\/\/www.microsoft.com\/en-us\/haxtoolkit\/\" target=\"_self\" aria-label=\"Explore the HAX Toolkit\" data-bi-type=\"annotated-link\" data-bi-cN=\"Explore the HAX Toolkit\" class=\"annotations__list-thumbnail\" >\n\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"172\" height=\"96\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Hax_toolkit_thumbnail_v2-240x135.jpg\" class=\"mb-2\" alt=\"abstract pattern background with the text \"HAX Toolkit\"\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Hax_toolkit_thumbnail_v2-240x135.jpg 240w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Hax_toolkit_thumbnail_v2-300x169.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Hax_toolkit_thumbnail_v2-1024x576.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Hax_toolkit_thumbnail_v2-768x432.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Hax_toolkit_thumbnail_v2-1066x600.jpg 1066w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Hax_toolkit_thumbnail_v2-655x368.jpg 655w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Hax_toolkit_thumbnail_v2-343x193.jpg 343w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Hax_toolkit_thumbnail_v2-640x360.jpg 640w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Hax_toolkit_thumbnail_v2-960x540.jpg 960w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Hax_toolkit_thumbnail_v2-1280x720.jpg 1280w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Hax_toolkit_thumbnail_v2.jpg 1400w\" sizes=\"(max-width: 172px) 100vw, 172px\" \/>\t\t\t\t<\/a>\n\t\t\t\t\t\t\t<span class=\"annotations__type d-block text-uppercase font-weight-semibold text-neutral-300 small\"><\/span>\n\t\t\t<a href=\"https:\/\/www.microsoft.com\/en-us\/haxtoolkit\/\" target=\"_self\" class=\"annotations__link font-weight-semibold text-decoration-none\" data-bi-type=\"annotated-link\" aria-label=\"Explore the HAX Toolkit\" data-bi-aN=\"margin-callout\" data-bi-cN=\"Explore the HAX Toolkit\">\n\t\t\t\tExplore the HAX Toolkit&nbsp;<span class=\"glyph-append glyph-append-share glyph-append-xsmall\"><\/span>\n\t\t\t<\/a>\n\t\t\t\t\t\t\t<p class=\"annotations__caption text-neutral-400 mt-2\">The Human-AI eXperience (HAX) Toolkit helps builders of AI systems create fluid, responsible human-AI experiences.<\/p>\n\t\t\t\t\t<\/li>\n\t<\/ul>\n<\/div>\n\n\n\n<div class=\"annotations \" data-bi-aN=\"margin-callout\">\n\t<ul class=\"annotations__list card depth-16 bg-body p-4 annotations__list--right\">\n\t\t<li class=\"annotations__list-item\">\n\t\t\t\t\t\t\t<a href=\"https:\/\/responsibleaitoolbox.ai\/\" target=\"_self\" aria-label=\"Explore the Responsible AI Toolbox\" data-bi-type=\"annotated-link\" data-bi-cN=\"Explore the Responsible AI Toolbox\" class=\"annotations__list-thumbnail\" >\n\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"172\" height=\"96\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Operationalize_thumbnail-240x135.jpg\" class=\"mb-2\" alt=\"Responsible AI Toolbox homepage\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Operationalize_thumbnail-240x135.jpg 240w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Operationalize_thumbnail-300x169.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Operationalize_thumbnail-1024x576.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Operationalize_thumbnail-768x432.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Operationalize_thumbnail-1066x600.jpg 1066w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Operationalize_thumbnail-655x368.jpg 655w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Operationalize_thumbnail-343x193.jpg 343w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Operationalize_thumbnail-640x360.jpg 640w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Operationalize_thumbnail-960x540.jpg 960w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Operationalize_thumbnail-1280x720.jpg 1280w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Operationalize_thumbnail.jpg 1400w\" sizes=\"(max-width: 172px) 100vw, 172px\" \/>\t\t\t\t<\/a>\n\t\t\t\t\t\t\t<span class=\"annotations__type d-block text-uppercase font-weight-semibold text-neutral-300 small\"><\/span>\n\t\t\t<a href=\"https:\/\/responsibleaitoolbox.ai\/\" target=\"_self\" class=\"annotations__link font-weight-semibold text-decoration-none\" data-bi-type=\"annotated-link\" aria-label=\"Explore the Responsible AI Toolbox\" data-bi-aN=\"margin-callout\" data-bi-cN=\"Explore the Responsible AI Toolbox\">\n\t\t\t\tExplore the Responsible AI Toolbox&nbsp;<span class=\"glyph-append glyph-append-share glyph-append-xsmall\"><\/span>\n\t\t\t<\/a>\n\t\t\t\t\t\t\t<p class=\"annotations__caption text-neutral-400 mt-2\">Customizable dashboards that help builders of AI systems identify, diagnose, and mitigate model errors, as well as debug models and understand causal relationships in data.<\/p>\n\t\t\t\t\t<\/li>\n\t<\/ul>\n<\/div>\n\n\n\n<p>The following is a sampling of research from the past year representing efforts across the Microsoft responsible AI ecosystem that highlight ways for creating appropriate trust in AI. Facilitating trustworthy measurement, improving human-AI collaboration, designing for natural language processing (NLP), advancing transparency and interpretability, and exploring the open questions around AI safety, security, and privacy are key considerations for developing AI responsibly. The goal of trustworthy AI requires a shift in perspective at every stage of the AI development and deployment life cycle. We\u2019re actively developing a growing number of <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/blogs.microsoft.com\/ai-for-business\/building-ai-responsibly-from-research-to-practice\/\" target=\"_blank\" rel=\"noreferrer noopener\">best practices and tools<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> to help with the shift to make responsible AI more available to a broader base of users. Many open questions remain, but as innovators, we are committed to tackling these challenges with curiosity, enthusiasm, and humility.&nbsp;<\/p>\n\n\n<aside id=accordion-d812fd82-357b-46d9-a9e1-2ff451a5d440 class=\"msr-table-of-contents-block accordion mb-5 pb-0\" data-bi-aN=\"table-of-contents\">\n\t<button class=\"btn btn-collapse bg-gray-100 mb-0 display-flex justify-content-between\" type=\"button\" data-mount=\"collapse\" data-target=\"#accordion-collapse-d812fd82-357b-46d9-a9e1-2ff451a5d440\" aria-expanded=\"true\" aria-controls=\"accordion-collapse-d812fd82-357b-46d9-a9e1-2ff451a5d440\">\n\t\t<span class=\"msr-table-of-contents-block__label subtitle\">In this article<\/span>\n\t\t<span class=\"msr-table-of-contents-block__current mr-4 text-gray-600 font-weight-normal\" aria-hidden=\"true\"><\/span>\n\t<\/button>\n\t<div id=\"accordion-collapse-d812fd82-357b-46d9-a9e1-2ff451a5d440\" class=\"msr-table-of-contents-block__collapse-wrapper collapse show\" data-parent=\"#accordion-d812fd82-357b-46d9-a9e1-2ff451a5d440\">\n\t\t<div class=\"accordion-body bg-gray-100 border-top pt-4\">\n\t\t\t<ol class=\"msr-table-of-contents-block__list\">\n\t\t\t\t\t\t\t\t\t<li class=\"msr-table-of-contents-block__list-item\">\n\t\t\t\t\t\t<a href=\"#facilitating-trustworthy-measurement\" class=\"msr-table-of-contents-block__list-item-link\">Facilitating trustworthy measurement<\/a>\n\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<li class=\"msr-table-of-contents-block__list-item\">\n\t\t\t\t\t\t<a href=\"#improving-human-ai-collaboration\" class=\"msr-table-of-contents-block__list-item-link\">Improving human-AI collaboration<\/a>\n\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<li class=\"msr-table-of-contents-block__list-item\">\n\t\t\t\t\t\t<a href=\"#designing-for-natural-language-processing\" class=\"msr-table-of-contents-block__list-item-link\">Designing for natural language processing\u00a0<\/a>\n\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<li class=\"msr-table-of-contents-block__list-item\">\n\t\t\t\t\t\t<a href=\"#improving-transparency\" class=\"msr-table-of-contents-block__list-item-link\">Improving transparency<\/a>\n\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<li class=\"msr-table-of-contents-block__list-item\">\n\t\t\t\t\t\t<a href=\"#advancing-algorithms-for-interpretability\" class=\"msr-table-of-contents-block__list-item-link\">Advancing algorithms for interpretability<\/a>\n\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<li class=\"msr-table-of-contents-block__list-item\">\n\t\t\t\t\t\t<a href=\"#exploring-open-questions-for-safety-security-and-privacy-in-ai\" class=\"msr-table-of-contents-block__list-item-link\">Exploring open questions for safety, security, and privacy in AI<\/a>\n\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t<\/ul>\n\t\t<\/div>\n\t<\/div>\n\t<span class=\"msr-table-of-contents-block__progress-bar\"><\/span>\n<\/aside>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"facilitating-trustworthy-measurement\">Facilitating trustworthy measurement<\/h2>\n\n\n\n<figure class=\"wp-block-embed aligncenter is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Ensuring our measurements capture what we think they are capturing\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/OaUk2DJajRo?feature=oembed&rel=0\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><figcaption>Emre Kiciman, co-chair of the Aether Security working group: Ensuring our measurements capture what we <em>think<\/em> they\u2019re capturing<\/figcaption><\/figure>\n\n\n\n<p>AI technologies influence the world through the connection of machine learning models\u2014that provide classifications, diagnoses, predictions, and recommendations\u2014with larger systems that drive displays, guide controls, and activate effectors. But when we use AI to help us understand patterns in human behavior and complex societal phenomena, we need to be vigilant. By creating models for assessing or measuring human behavior, we\u2019re participating in the very act of shaping society. <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/responsible-computing-during-covid-19-and-beyond\/\">Guidelines for ethically navigating technology\u2019s impacts<\/a> on society\u2014guidance born out of considering technologies for COVID-19\u2014prompt us to start by weighing a project&#8217;s risk of harm against its benefits. Sometimes an important step in the practice of responsible AI may be the decision to not build a particular model or application.&nbsp;<\/p>\n\n\n\n<p>Human behavior and algorithms influence each other in feedback loops. In a recent <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/measuring-algorithmically-infused-societies\/\"><em>Nature <\/em>publication<\/a>, Microsoft researchers and collaborators emphasize that existing methods for measuring social phenomena may not be up to the task of investigating societies where human behavior and algorithms affect each other. They offer five best practices for advancing computational social science. These include developing measurement models that are informed by social theory and that are fair, transparent, interpretable, and privacy preserving. For trustworthy measurement, it\u2019s crucial to document and justify the model\u2019s underlying assumptions, plus consider <em>who<\/em> is deciding <em>what<\/em> to measure and how those results will be used.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><a data-bi-bhvr=\"14\"  data-bi-cn=\"5 Best practices for measuring algorithmically infused societies\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Best_practices_responsibleAi_V2.gif\"><img loading=\"lazy\" decoding=\"async\" width=\"1400\" height=\"788\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Best_practices_responsibleAi_V2.gif\" alt=\"5 Best practices for measuring algorithmically infused societies\" class=\"wp-image-816880\"\/><span class=\"sr-only\"> (opens in new tab)<\/span><\/a><figcaption> Source: Adapted from <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" data-bi-bhvr=\"14\"  data-bi-cn=\"5 Best practices for measuring algorithmically infused societies\" href=\"https:\/\/www.nature.com\/articles\/s41586-021-03666-1\">Nature<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/figcaption><\/figure>\n\n\n\n<p>In line with these best practices, Microsoft researchers and collaborators have proposed <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/measurement-and-fairness\/\">measurement modeling as a framework for anticipating and mitigating fairness-related harms caused by AI systems<\/a>. This framework can help identify mismatches between theoretical understandings of abstract concepts\u2014for example, socioeconomic status\u2014and how these concepts get translated into mathematics and code. Identifying mismatches helps AI practitioners to anticipate and mitigate fairness-related harms that reinforce societal biases and inequities. A study <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/stereotyping-norwegian-salmon-an-inventory-of-pitfalls-in-fairness-benchmark-datasets\/\">applying a measurement modeling lens to several benchmark datasets for surfacing stereotypes in NLP systems reveals considerable ambiguity and hidden assumptions<\/a>, demonstrating (among other things) that datasets widely trusted for measuring the presence of stereotyping can, in fact, cause stereotyping harms.<\/p>\n\n\n\n<div class=\"annotations \" data-bi-aN=\"margin-callout\">\n\t<ul class=\"annotations__list card depth-16 bg-body p-4 annotations__list--right\">\n\t\t<li class=\"annotations__list-item\">\n\t\t\t\t\t\t\t<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/failures-of-imagination-discovering-and-measuring-harms-in-language-technologies\/\" target=\"_self\" aria-label=\"Discovering and measuring harms in NLP\" data-bi-type=\"annotated-link\" data-bi-cN=\"Discovering and measuring harms in NLP\" class=\"annotations__list-thumbnail\" >\n\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"172\" height=\"96\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/MSR_Alexandra_SuLin_Webinar_thumbnail_1400x788-240x135.jpg\" class=\"mb-2\" alt=\"two headshots of alex and su lin next to their webinar title\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/MSR_Alexandra_SuLin_Webinar_thumbnail_1400x788-240x135.jpg 240w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/MSR_Alexandra_SuLin_Webinar_thumbnail_1400x788-300x169.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/MSR_Alexandra_SuLin_Webinar_thumbnail_1400x788-1024x576.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/MSR_Alexandra_SuLin_Webinar_thumbnail_1400x788-768x432.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/MSR_Alexandra_SuLin_Webinar_thumbnail_1400x788-1066x600.jpg 1066w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/MSR_Alexandra_SuLin_Webinar_thumbnail_1400x788-655x368.jpg 655w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/MSR_Alexandra_SuLin_Webinar_thumbnail_1400x788-343x193.jpg 343w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/MSR_Alexandra_SuLin_Webinar_thumbnail_1400x788-640x360.jpg 640w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/MSR_Alexandra_SuLin_Webinar_thumbnail_1400x788-960x540.jpg 960w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/MSR_Alexandra_SuLin_Webinar_thumbnail_1400x788-1280x720.jpg 1280w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/MSR_Alexandra_SuLin_Webinar_thumbnail_1400x788.jpg 1400w\" sizes=\"(max-width: 172px) 100vw, 172px\" \/>\t\t\t\t<\/a>\n\t\t\t\t\t\t\t<span class=\"annotations__type d-block text-uppercase font-weight-semibold text-neutral-300 small\">VIDEO<\/span>\n\t\t\t<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/failures-of-imagination-discovering-and-measuring-harms-in-language-technologies\/\" target=\"_self\" class=\"annotations__link font-weight-semibold text-decoration-none\" data-bi-type=\"annotated-link\" aria-label=\"Discovering and measuring harms in NLP\" data-bi-aN=\"margin-callout\" data-bi-cN=\"Discovering and measuring harms in NLP\">\n\t\t\t\tDiscovering and measuring harms in NLP&nbsp;<span class=\"glyph-append glyph-append-chevron-right glyph-append-xsmall\"><\/span>\n\t\t\t<\/a>\n\t\t\t\t\t\t\t<p class=\"annotations__caption text-neutral-400 mt-2\">Examining pitfalls in state-of-the-art approaches to measuring computational harms in language technologies.<\/p>\n\t\t\t\t\t<\/li>\n\t<\/ul>\n<\/div>\n\n\n\n<p>Flaws in datasets can lead to AI systems with unfair outcomes, such as poor quality of service or denial of opportunities and resources for different groups of people. AI practitioners need to understand how their systems are performing for <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/understanding-the-representation-and-representativeness-of-age-in-ai-data-sets\/\">factors like age<\/a>, race, gender, and socioeconomic status so they can mitigate potential harms. In identifying <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/designing-disaggregated-evaluations-of-ai-systems-choices-considerations-and-tradeoffs\/\">the decisions that AI practitioners must make when evaluating an AI system\u2019s performance for different groups of people<\/a>, researchers highlight the importance of rigor in the construction of evaluation datasets.&nbsp;<\/p>\n\n\n\n<p>Making sure that datasets are representative and inclusive means facilitating data collection from different groups of people, including people with disabilities. Mainstream AI systems are often non-inclusive. For example, speech recognition systems do not work for atypical speech, while input devices are not accessible for people with limited mobility. In pursuit of inclusive AI, a study proposes <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/designing-an-online-infrastructure-for-collecting-ai-data-from-people-with-disabilities\/\">guidelines for designing an accessible online infrastructure for collecting data from people with disabilities<\/a>, one that is built to respect, protect, and motivate those contributing data.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"related-papers\">Related papers<\/h3>\n\n\n\n<ul class=\"wp-block-list\"><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/responsible-computing-during-covid-19-and-beyond\/\">Responsible computing during COVID-19 and beyond<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/measuring-algorithmically-infused-societies\/\">Measuring algorithmically infused societies<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/measurement-and-fairness\/\">Measurement and fairness<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/stereotyping-norwegian-salmon-an-inventory-of-pitfalls-in-fairness-benchmark-datasets\/\">Stereotyping Norwegian salmon: An inventory of pitfalls in fairness benchmark datasets<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/understanding-the-representation-and-representativeness-of-age-in-ai-data-sets\/\">Understanding the representation and representativeness of age in AI data sets<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/designing-disaggregated-evaluations-of-ai-systems-choices-considerations-and-tradeoffs\/\">Designing disaggregated evaluations of AI systems: Choices, considerations, and tradeoffs<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/designing-an-online-infrastructure-for-collecting-ai-data-from-people-with-disabilities\/\">Designing an online infrastructure for collecting AI data from People with Disabilities<\/a><\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"improving-human-ai-collaboration\">Improving human-AI collaboration<\/h2>\n\n\n\n<figure class=\"wp-block-embed aligncenter is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Investing in research and new techniques for effective human-AI partnership\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/imzeCdoPsoM?feature=oembed&rel=0\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><figcaption>Ece Kamar, Aether technical advisor and co-chair of the Aether Reliability and Safety working group: Investing in research and new techniques for effective human-AI partnership<\/figcaption><\/figure>\n\n\n\n<p>When people and AI collaborate on solving problems, the benefits can be impressive. But current practice can be far from establishing a successful partnership between people and AI systems. A promising advance and direction of research is <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/learning-to-complement-humans\/\">developing methods that learn about ideal ways to complement people with problem solving<\/a>. In the approach, machine learning models are optimized to detect where people need the most help versus where people can solve problems well on their own. We can additionally train the AI systems to make decisions as to when a system should ask an individual for input and to combine the human and machine abilities to make a recommendation. In related work, studies have shown that people will too often accept an AI system\u2019s outputs without question, relying on them even when they are wrong. Exploring how to facilitate appropriate trust in human-AI teamwork, experiments with real-world datasets for AI systems show that <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/is-the-most-accurate-ai-the-best-teammate-optimizing-ai-for-teamwork\/\">retraining a model with a human-centered approach can better optimize human-AI team performance<\/a>. This means taking into account human accuracy, human effort, the cost of mistakes\u2014and people\u2019s mental models of the AI.&nbsp;<\/p>\n\n\n\n<p>In systems for healthcare and other high-stakes scenarios, a break with the user\u2019s mental model can have severe impacts. An AI system can compromise trust when, after an update for better overall accuracy, it begins to underperform in some areas. For instance, an updated system for predicting cancerous skin moles may have an increase in accuracy overall but a significant decrease for facial moles. A physician using the system may either lose confidence in the benefits of the technology or, with more dire consequences, may not notice this drop in performance. Techniques for forcing an updated system to be compatible with a previous version produce tradeoffs in accuracy. But experiments demonstrate that <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/improving-the-performance-compatibility-tradeoff-with-personalized-objective-functions\/\">personalizing objective functions can improve the performance-compatibility tradeoff<\/a> for specific users by as much as 300 percent.<\/p>\n\n\n\n<p>System updates can have grave consequences when it comes to algorithms used for prescribing recourse, such as how to fix a bad credit score to qualify for a loan. Updates can lead to people who have dutifully followed a prescribed recourse being denied their promised rights or services and damaging their trust in decision makers. Examining the impact of updates caused by changes in the data distribution, researchers <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/algorithmic-recourse-in-the-wild-understanding-the-impact-of-data-and-model-shifts\/\">expose previously unknown flaws in the current recourse-generation paradigm<\/a>. This work points toward rethinking how to design these algorithms for robustness and reliability.&nbsp;<\/p>\n\n\n\n<p>Complementarity in human-AI performance, where the human-AI team performs better together by compensating for each other\u2019s weaknesses, is a goal for AI-assisted tasks. You might think that if a system provided an explanation of its output, this could help an individual identify and correct an AI failure, producing the best of human-AI teamwork. Surprisingly, and in contrast to prior work, a <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/does-the-whole-exceed-its-parts-the-effect-of-ai-explanations-on-complementary-team-performance\/\">large-scale study shows that explanations may not significantly increase human-AI team performance<\/a>. People often over-rely on recommendations even when the AI is incorrect. This is a call to action: we need to develop methods for communicating explanations that increase users\u2019 understanding rather than to just persuade.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"related-papers\">Related papers<\/h3>\n\n\n\n<ul class=\"wp-block-list\"><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/learning-to-complement-humans\/\">Learning to complement humans<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/is-the-most-accurate-ai-the-best-teammate-optimizing-ai-for-teamwork\/\">Is the most accurate AI the best teammate? Optimizing AI for teamwork<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/improving-the-performance-compatibility-tradeoff-with-personalized-objective-functions\/\">Improving the performance-compatibility tradeoff with personalized objective functions<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/algorithmic-recourse-in-the-wild-understanding-the-impact-of-data-and-model-shifts\/\">Algorithmic recourse in the wild: Understanding the impact of data and model shift<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/does-the-whole-exceed-its-parts-the-effect-of-ai-explanations-on-complementary-team-performance\/\">Does the whole exceed its parts? The effect of AI explanations on complementary team performance<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-bayesian-approach-to-identifying-representational-errors\/\">A Bayesian approach to identifying representational errors<\/a><\/li><\/ul>\n\n\n\n\t<div class=\"border-bottom border-top border-gray-300 mt-5 mb-5 msr-promo text-center text-md-left alignwide\" data-bi-aN=\"promo\" data-bi-id=\"1085508\">\n\t\t\n\n\t\t<p class=\"msr-promo__label text-gray-800 text-center text-uppercase\">\n\t\t<span class=\"px-4 bg-white display-inline-block font-weight-semibold small\">Spotlight: Blog post<\/span>\n\t<\/p>\n\t\n\t<div class=\"row pt-3 pb-4 align-items-center\">\n\t\t\t\t\t\t<div class=\"msr-promo__media col-12 col-md-5\">\n\t\t\t\t<a class=\"bg-gray-300\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/blog\/medfuzz-exploring-the-robustness-of-llms-on-medical-challenge-problems\/\" aria-label=\"MedFuzz: Exploring the robustness of LLMs on medical challenge problems\" data-bi-cN=\"MedFuzz: Exploring the robustness of LLMs on medical challenge problems\" target=\"_blank\">\n\t\t\t\t\t<img decoding=\"async\" class=\"w-100 display-block\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz-BlogHeroFeature-1400x788-1.jpg\" alt=\"MedFuzz blog hero (decorative)\" \/>\n\t\t\t\t<\/a>\n\t\t\t<\/div>\n\t\t\t\n\t\t\t<div class=\"msr-promo__content p-3 px-5 col-12 col-md\">\n\n\t\t\t\t\t\t\t\t\t<h2 class=\"h4\">MedFuzz: Exploring the robustness of LLMs on medical challenge problems<\/h2>\n\t\t\t\t\n\t\t\t\t\t\t\t\t<p class=\"large\">Medfuzz tests LLMs by breaking benchmark assumptions, exposing vulnerabilities to bolster real-world accuracy.<\/p>\n\t\t\t\t\n\t\t\t\t\t\t\t\t<div class=\"wp-block-buttons justify-content-center justify-content-md-start\">\n\t\t\t\t\t<div class=\"wp-block-button\">\n\t\t\t\t\t\t<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/blog\/medfuzz-exploring-the-robustness-of-llms-on-medical-challenge-problems\/\" class=\"btn btn-brand glyph-append glyph-append-chevron-right\" aria-label=\"Read more\" data-bi-cN=\"MedFuzz: Exploring the robustness of LLMs on medical challenge problems\" target=\"_blank\">\n\t\t\t\t\t\t\tRead more\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div><!--\/.msr-promo__content-->\n\t<\/div><!--\/.msr-promo__inner-wrap-->\n\t<\/div><!--\/.msr-promo-->\n\t\n\n\n<h2 class=\"wp-block-heading\" id=\"designing-for-natural-language-processing\">Designing for natural language processing&nbsp;<\/h2>\n\n\n\n<figure class=\"wp-block-embed aligncenter is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Developing natural language processing models in a responsible manner\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/7Kkyt0bwfDg?feature=oembed&rel=0\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><figcaption>Hanna Wallach, Aether technical advisor and co-chair of the Aether Fairness and Inclusiveness working group: Developing natural language processing models in a responsible manner<\/figcaption><\/figure>\n\n\n\n<p>The allure of natural language processing\u2019s potential, including rash claims of human parity, raises questions of how we can employ NLP technologies in ways that are truly useful, as well as fair and inclusive. To further these and other goals, Microsoft researchers and collaborators hosted the <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/bridging-human-computer-interaction-and-natural-language-processing\/\">first workshop on bridging human-computer interaction and natural language processing<\/a>, considering novel questions and research directions for designing NLP systems to align with people\u2019s demonstrated needs.&nbsp;<\/p>\n\n\n\n<p>Language shapes minds and societies. Technology that wields this power requires scrutiny as to what harms may ensue. For example, does an NLP system exacerbate stereotyping? Does it exhibit the same quality of service for people who speak the same language in different ways? A survey of 146 papers analyzing \u201cbias\u201d in NLP observes rampant pitfalls of unstated assumptions and conceptualizations of bias. To avoid these pitfalls, the authors outline <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/language-technology-is-power-a-critical-survey-of-bias-in-nlp\/\">recommendations based on the recognition of relationships between language and social hierarchies<\/a> as fundamentals for fairness in the context of NLP. We must be precise in how we articulate ideas about fairness if we are to identify, measure, and mitigate NLP systems\u2019 potential for fairness-related harms.&nbsp;<\/p>\n\n\n\n<div class=\"annotations \" data-bi-aN=\"margin-callout\">\n\t<ul class=\"annotations__list card depth-16 bg-body p-4 annotations__list--left\">\n\t\t<li class=\"annotations__list-item\">\n\t\t\t\t\t\t\t<a href=\"https:\/\/microsoft.github.io\/HAXPlaybook\/\" target=\"_self\" aria-label=\"Launch the HAX Playbook\" data-bi-type=\"annotated-link\" data-bi-cN=\"Launch the HAX Playbook\" class=\"annotations__list-thumbnail\" >\n\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"172\" height=\"96\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Ai_feature_thumbnail-240x135.jpg\" class=\"mb-2\" alt=\"graphical user interface, text, application\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Ai_feature_thumbnail-240x135.jpg 240w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Ai_feature_thumbnail-300x169.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Ai_feature_thumbnail-1024x577.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Ai_feature_thumbnail-768x433.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Ai_feature_thumbnail-1066x600.jpg 1066w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Ai_feature_thumbnail-655x368.jpg 655w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Ai_feature_thumbnail-343x193.jpg 343w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Ai_feature_thumbnail-640x360.jpg 640w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Ai_feature_thumbnail-960x540.jpg 960w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Ai_feature_thumbnail-1280x720.jpg 1280w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Ai_feature_thumbnail.jpg 1400w\" sizes=\"(max-width: 172px) 100vw, 172px\" \/>\t\t\t\t<\/a>\n\t\t\t\t\t\t\t<span class=\"annotations__type d-block text-uppercase font-weight-semibold text-neutral-300 small\"><\/span>\n\t\t\t<a href=\"https:\/\/microsoft.github.io\/HAXPlaybook\/\" target=\"_self\" class=\"annotations__link font-weight-semibold text-decoration-none\" data-bi-type=\"annotated-link\" aria-label=\"Launch the HAX Playbook\" data-bi-aN=\"margin-callout\" data-bi-cN=\"Launch the HAX Playbook\">\n\t\t\t\tLaunch the HAX Playbook&nbsp;<span class=\"glyph-append glyph-append-share glyph-append-xsmall\"><\/span>\n\t\t\t<\/a>\n\t\t\t\t\t\t\t<p class=\"annotations__caption text-neutral-400 mt-2\">A low-cost way to proactively identify, design for, and test human-AI interaction failures.<\/p>\n\t\t\t\t\t<\/li>\n\t<\/ul>\n<\/div>\n\n\n\n<p>The open-ended nature of language\u2014its inherent ambiguity, context-dependent meaning, and constant evolution\u2014drives home the need to plan for failures when developing NLP systems. <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/planning-for-natural-language-failures-with-the-ai-playbook\/\">Planning for NLP failures with the AI Playbook<\/a> introduces a <a href=\"https:\/\/www.microsoft.com\/en-us\/haxtoolkit\/playbook\/\" target=\"_blank\" rel=\"noreferrer noopener\">new tool<\/a> for AI practitioners to anticipate errors and plan human-AI interaction so that the user experience is not severely disrupted when errors inevitably occur.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"related-papers\">Related papers<\/h3>\n\n\n\n<ul class=\"wp-block-list\"><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/bridging-human-computer-interaction-and-natural-language-processing\/\">Proceedings of the first workshop on bridging human-computer interaction and natural language processing<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/language-technology-is-power-a-critical-survey-of-bias-in-nlp\/\">Language (technology) is power: A critical survey of \u201cbias\u201d in NLP<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/planning-for-natural-language-failures-with-the-ai-playbook\/\">Planning for natural language failures with the AI Playbook<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/invariant-language-modeling\/\">Invariant language modeling<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/on-the-relationships-between-the-grammatical-genders-of-inanimate-nouns-and-their-co-occurring-adjectives-and-verbs\/\">On the relationships between the grammatical genders of inanimate nouns and their co-occurring adjectives and verbs<\/a><\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"improving-transparency\">Improving transparency<\/h2>\n\n\n\n<figure class=\"wp-block-embed aligncenter is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Providing stakeholders with an appropriate understanding of how AI systems work\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/8xdkbuB_Gvs?feature=oembed&rel=0\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><figcaption>Jenn Wortman Vaughan, co-chair of the Aether Transparency working group: Providing stakeholders with an appropriate understanding of how AI systems work<\/figcaption><\/figure>\n\n\n\n<p>To build AI systems that are reliable and fair\u2014and to assess how much to trust them\u2014practitioners and those using these systems need insight into their behavior. If we are to meet the goal of AI transparency, the <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-human-centered-agenda-for-intelligible-machine-learning\/\">AI\/ML and human-computer interaction communities need to integrate efforts to create human-centered interpretability methods<\/a> that yield explanations that can be clearly understood and are actionable by people using AI systems in real-world scenarios.&nbsp;<\/p>\n\n\n\n<p>As a case in point, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/manipulating-and-measuring-model-interpretability\/\">experiments investigating whether simple models that are thought to be interpretable achieve their intended effects<\/a> rendered counterintuitive findings. When participants used an ML model considered to be interpretable to help them predict the selling prices of New York City apartments, they had difficulty detecting when the model was demonstrably wrong. Providing too many details of the model\u2019s internals seemed to distract and cause information overload. <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/summarize-with-caution-comparing-global-feature-attributions\/\">Another recent study<\/a> found that even when an explanation helps data scientists gain a more nuanced understanding of a model, they may be unwilling to make the effort to understand it if it slows down their workflow too much. As both studies show, testing with users is essential to see if people clearly understand and can use a model\u2019s explanations to their benefit. User research is the only way to validate what is or is not interpretable by people using these systems.<\/p>\n\n\n\n<p>Explanations that are meaningful to people using AI systems are key to the transparency and interpretability of black-box models. Introducing a <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/from-human-explanation-to-model-interpretability-a-framework-based-on-weight-of-evidence\/\">weight-of-evidence approach to creating machine-generated explanations that are meaningful to people<\/a>, Microsoft researchers and colleagues highlight the importance of designing explanations with people\u2019s needs in mind and evaluating how people use interpretability tools and what their understanding is of the underlying concepts. The paper also underscores the need to provide well-designed tutorials.<\/p>\n\n\n\n<p>Traceability and communication are also fundamental for demonstrating trustworthiness. Both AI practitioners and people using AI systems benefit from knowing the motivation and composition of datasets. Tools such as <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/datasheets-for-datasets\/\">datasheets for datasets<\/a> prompt AI dataset creators to carefully reflect on the process of creation, including any underlying assumptions they are making and potential risks or harms that might arise from the dataset&#8217;s use. And for dataset consumers, seeing the dataset creators\u2019 documentation of goals and assumptions equips them to decide whether a dataset is suitable for the task they have in mind.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"related-papers\">Related papers<\/h3>\n\n\n\n<ul class=\"wp-block-list\"><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-human-centered-agenda-for-intelligible-machine-learning\/\">A human-centered agenda for intelligible machine learning<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/datasheets-for-datasets\/\">Datasheets for datasets<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/manipulating-and-measuring-model-interpretability\/\">Manipulating and measuring model interpretability<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/from-human-explanation-to-model-interpretability-a-framework-based-on-weight-of-evidence\/\">From human explanation to model interpretability: A framework based on weight of evidence<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/summarize-with-caution-comparing-global-feature-attributions\/\">Summarize with caution: Comparing global feature attributions<\/a><\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"advancing-algorithms-for-interpretability\">Advancing algorithms for interpretability<\/h2>\n\n\n\n<figure class=\"wp-block-embed aligncenter is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Interpretability shows how much trust to put in your AI models\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/T8nzhQkZpJQ?feature=oembed&rel=0\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><figcaption>Rich Caruana, co-chair of the Aether Transparency working group: Demonstrating how interpretability shows how much trust to put in your AI models<\/figcaption><\/figure>\n\n\n\n<p>Interpretability is vital to debugging and mitigating the potentially harmful impacts of AI processes that so often take place in seemingly impenetrable black boxes\u2014it is difficult (and in many settings, inappropriate) to trust an AI model if you can\u2019t understand the model and correct it when it is wrong. Advanced glass-box learning algorithms can enable AI practitioners and stakeholders to see what\u2019s \u201cunder the hood\u201d and better understand the behavior of AI systems. And advanced user interfaces can make it easier for people using AI systems to understand these models and then edit the models when they find mistakes or bias in them. Interpretability is also important to improve human-AI collaboration\u2014it is difficult for users to interact and collaborate with an AI model or system if they can\u2019t understand it. At Microsoft, we have developed glass-box learning methods that are now as accurate as previous black-box methods but yield AI models that are fully interpretable and editable.&nbsp;<\/p>\n\n\n\n<div class=\"annotations \" data-bi-aN=\"margin-callout\">\n\t<ul class=\"annotations__list card depth-16 bg-body p-4 annotations__list--right\">\n\t\t<li class=\"annotations__list-item\">\n\t\t\t\t\t\t\t<a href=\"https:\/\/youtu.be\/NS4rQ-SfDjg\" target=\"_self\" aria-label=\"Editing GAMs with interactive visualization\" data-bi-type=\"annotated-link\" data-bi-cN=\"Editing GAMs with interactive visualization\" class=\"annotations__list-thumbnail\" >\n\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"172\" height=\"96\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/gam-changer-demo2-240x135.png\" class=\"mb-2\" alt=\"GAM Changer Demo\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/gam-changer-demo2-240x135.png 240w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/gam-changer-demo2-300x169.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/gam-changer-demo2-1024x576.png 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/gam-changer-demo2-768x432.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/gam-changer-demo2-1066x600.png 1066w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/gam-changer-demo2-655x368.png 655w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/gam-changer-demo2-343x193.png 343w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/gam-changer-demo2-640x360.png 640w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/gam-changer-demo2-960x540.png 960w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/gam-changer-demo2-1280x720.png 1280w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/gam-changer-demo2.png 1400w\" sizes=\"(max-width: 172px) 100vw, 172px\" \/>\t\t\t\t<\/a>\n\t\t\t\t\t\t\t<span class=\"annotations__type d-block text-uppercase font-weight-semibold text-neutral-300 small\">VIDEO<\/span>\n\t\t\t<a href=\"https:\/\/youtu.be\/NS4rQ-SfDjg\" target=\"_self\" class=\"annotations__link font-weight-semibold text-decoration-none\" data-bi-type=\"annotated-link\" aria-label=\"Editing GAMs with interactive visualization\" data-bi-aN=\"margin-callout\" data-bi-cN=\"Editing GAMs with interactive visualization\">\n\t\t\t\tEditing GAMs with interactive visualization&nbsp;<span class=\"glyph-append glyph-append-share glyph-append-xsmall\"><\/span>\n\t\t\t<\/a>\n\t\t\t\t\t\t\t<p class=\"annotations__caption text-neutral-400 mt-2\">Machine learning interpretability techniques reveal that many accurate models learn some problematic and dangerous patterns from the training data. GAM Changer helps address these issues.<\/p>\n\t\t\t\t\t<\/li>\n\t<\/ul>\n<\/div>\n\n\n\n<p>Recent advances at Microsoft include a <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/node-gam-neural-generalized-additive-model-for-interpretable-deep-learning\/\">new neural GAM (generalized additive model) for interpretable deep learning<\/a>, a method for <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/dropout-as-a-regularizer-of-interaction-effects\/\">using dropout rates to reduce spurious interaction<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/purifying-interaction-effects-with-the-functional-anova-an-efficient-algorithm-for-recovering-identifiable-additive-models\/\">an efficient algorithm for recovering identifiable additive models<\/a>, the development of <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/accuracy-interpretability-and-differential-privacy-via-explainable-boosting\/\">glass-box models that are differentially private<\/a>, and the creation of <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/gam-changer-editing-generalized-additive-models-with-interactive-visualization\/\">tools that make editing glass-box models easy for those using them<\/a> so they can correct errors in the models and mitigate bias.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"related-papers\">Related papers<\/h3>\n\n\n\n<ul class=\"wp-block-list\"><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/node-gam-neural-generalized-additive-model-for-interpretable-deep-learning\/\">NODE-GAM: Neural generalized additive model for interpretable deep learning<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/dropout-as-a-regularizer-of-interaction-effects\/\">Dropout as a regularizer of interaction effects<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/purifying-interaction-effects-with-the-functional-anova-an-efficient-algorithm-for-recovering-identifiable-additive-models\/\">Purifying interaction effects with the Functional ANOVA: An efficient algorithm for recovering identifiable additive models<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/accuracy-interpretability-and-differential-privacy-via-explainable-boosting\/\">Accuracy, interpretability, and differential privacy via explainable boosting<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/gam-changer-editing-generalized-additive-models-with-interactive-visualization\/\">GAM changer: Editing generalized additive models with interactive visualization<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/neural-additive-models-interpretable-machine-learning-with-neural-nets\/\">Neural additive models: Interpretable machine learning with neural nets<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/how-interpretable-and-trustworthy-are-gams\/\">How interpretable and trustworthy are GAMs?<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/extracting-clinicians-goals-by-what-if-interpretable-modeling\/\">Extracting clinician&#8217;s goals by What-if interpretable modeling<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/automated-interpretable-discovery-of-heterogeneous-treatment-effectiveness-a-covid-19-case-study\/\">Automated interpretable discovery of heterogeneous treatment effectiveness: A Covid-19 case study<\/a><\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"exploring-open-questions-for-safety-security-and-privacy-in-ai\">Exploring open questions for safety, security, and privacy in AI<\/h2>\n\n\n\n<figure class=\"wp-block-embed aligncenter is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"AI's significant new challenges to reliability, security, and privacy\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/_58stdy4bbg?feature=oembed&rel=0\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><figcaption>Ben Zorn, co-chair of the Aether Reliability and Safety working group: Considering AI\u2019s significant new challenges to reliability, security, and privacy<\/figcaption><\/figure>\n\n\n\n<p>When considering how to shape appropriate trust in AI systems, there are many open questions about safety, security, and privacy. How do we stay a step ahead of attackers intent on subverting an AI system or harvesting its proprietary information? How can we avoid a system\u2019s potential for inferring spurious correlations?&nbsp;<\/p>\n\n\n\n<p>With autonomous systems, it is important to acknowledge that no system operating in the real world will ever be complete. It\u2019s impossible to train a system for the many unknowns of the real world. Unintended outcomes can range from annoying to dangerous. For example, a self-driving car may splash pedestrians on a rainy day or erratically swerve to localize itself for lane-keeping. An <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/avoiding-negative-side-effects-due-to-incomplete-knowledge-of-ai-systems\/\">overview of emerging research in avoiding negative side effects due to AI systems\u2019 incomplete knowledge<\/a> points to the importance of giving users the means to avoid or mitigate the undesired effects of an AI system\u2019s outputs as essential to how the technology will be viewed or used.&nbsp;<\/p>\n\n\n\n<p>When dealing with data about people and our physical world, privacy considerations take a vast leap in complexity. For example, it\u2019s possible for a malicious actor to isolate and re-identify individuals from information in large, anonymized datasets or from their interactions with online apps when using personal devices. Developments in privacy-preserving techniques face challenges in usability and adoption because of the deeply theoretical nature of concepts like homomorphic encryption, secure multiparty computation, and differential privacy. <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/exploring-design-and-governance-challenges-in-the-development-of-privacy-preserving-computation\/\">Exploring the design and governance challenges of privacy-preserving computation<\/a>, interviews with builders of AI systems, policymakers, and industry leaders reveal confidence that the technology is useful, but the challenge is to bridge the gap from theory to practice in real-world applications. Engaging the human-computer interaction community will be a critical component.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"related-papers\">Related papers<\/h3>\n\n\n\n<p><em>Reliability and safety<\/em><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/avoiding-negative-side-effects-due-to-incomplete-knowledge-of-ai-systems\/\">Avoiding negative side effects due to incomplete knowledge of AI systems<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/split-treatment-analysis-to-rank-heterogeneous-causal-effects-for-prospective-interventions\/\">Split-treatment analysis to rank heterogeneous causal effects for prospective interventions<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/out-of-distribution-prediction-with-invariant-risk-minimization-the-limitation-and-an-effective-fix\/\">Out-of-distribution prediction with invariant risk minimization: The limitation and an effective fix<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/causal-transfer-random-forest-combining-logged-data-and-randomized-experiments-for-robust-prediction\/\">Causal transfer random forest: Combining logged data and randomized experiments for robust prediction<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/understanding-failures-of-deep-networks-via-robust-feature-extraction\/\">Understanding failures of deep networks via robust feature extraction<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/dowhy-addressing-challenges-in-expressing-and-validating-causal-assumptions\/\">DoWhy: Addressing challenges in expressing and validating causal assumptions<\/a><\/li><\/ul>\n\n\n\n<p><em>Privacy and security<\/em>&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/exploring-design-and-governance-challenges-in-the-development-of-privacy-preserving-computation\/\">Exploring design and governance challenges in the development of privacy-preserving computation<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/accuracy-interpretability-and-differential-privacy-via-explainable-boosting\/\">Accuracy, interpretability, and differential privacy via explainable boosting<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/labeled-psi-from-homomorphic-encryption-with-reduced-computation-and-communication\/\">Labeled PSI from homomorphic encryption with reduced computation and communication<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/eva-improved-compiler-and-extension-library-for-ckks\/\">EVA improved: Compiler and extension library for CKKS<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/aggregate-measurement-via-oblivious-shuffling\/\">Aggregate measurement via oblivious shuffling<\/a><\/li><\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"a-call-to-personal-action\">A call to personal action<\/h3>\n\n\n\n<p>AI is not an end-all, be-all solution; it\u2019s a powerful, albeit fallible, set of technologies. The challenge is to maximize the benefits of AI while anticipating and minimizing potential harms.<\/p>\n\n\n\n<p>Admittedly, the goal of appropriate trust is challenging. Developing measurement tools for assessing a world in which algorithms are shaping our behaviors, exposing how systems arrive at decisions, planning for AI failures, and engaging the people on the receiving end of AI systems are important pieces. But what we do know is change can happen today with each one of us as we pause and reflect on our work, asking: what could go wrong, and what can <em>I<\/em> do to prevent it?&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Inflated expectations around the capabilities of AI technologies may lead people to believe that computers can\u2019t be wrong. The truth is AI failures are not a matter of if but when. AI is a human endeavor that combines information about people and the physical world into mathematical constructs. Such technologies typically rely on statistical methods, with the possibility for errors throughout an AI system\u2019s lifespan. As AI systems become more widely used across domains, especially in high-stakes scenarios where people\u2019s safety and wellbeing can be affected, a critical question must be addressed: how trustworthy are AI systems, and how much and when should people trust AI?\u00a0<\/p>\n","protected":false},"author":39507,"featured_media":815860,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"categories":[1],"tags":[],"research-area":[13556,13558],"msr-region":[],"msr-event-type":[],"msr-locale":[268875],"msr-post-option":[243984],"msr-impact-theme":[],"msr-promo-type":[],"msr-podcast-series":[],"class_list":["post-815857","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research-blog","msr-research-area-artificial-intelligence","msr-research-area-security-privacy-cryptography","msr-locale-en_us","msr-post-option-blog-homepage-featured"],"msr_event_details":{"start":"","end":"","location":""},"podcast_url":"","podcast_episode":"","msr_research_lab":[],"msr_impact_theme":[],"related-publications":[],"related-downloads":[],"related-videos":[],"related-academic-programs":[],"related-groups":[],"related-projects":[],"related-events":[],"related-researchers":[{"type":"user_nicename","value":"Mihaela Vorvoreanu","user_id":36804,"display_name":"Mihaela Vorvoreanu","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/mivorvor\/\" aria-label=\"Visit the profile page for Mihaela Vorvoreanu\">Mihaela Vorvoreanu<\/a>","is_active":false,"last_first":"Vorvoreanu, Mihaela","people_section":0,"alias":"mivorvor"}],"msr_type":"Post","featured_image_thumbnail":"<img width=\"960\" height=\"540\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog-960x540.jpg\" class=\"img-object-cover\" alt=\"blue graphic with a light honeycomb pattern background featuring a lightbulb in the middle and various icons around it: handshake, eye, connected people, balanced scale, lock, and shield\" decoding=\"async\" loading=\"lazy\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog-960x540.jpg 960w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog-300x169.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog-1024x577.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog-768x433.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog-1066x600.jpg 1066w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog-655x368.jpg 655w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog-343x193.jpg 343w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog-240x135.jpg 240w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog-640x360.jpg 640w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog-1280x720.jpg 1280w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2022\/01\/1400x788_Rai_hero_final_Blog.jpg 1401w\" sizes=\"(max-width: 960px) 100vw, 960px\" \/>","byline":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/mivorvor\/\" title=\"Go to researcher profile for Mihaela Vorvoreanu\" aria-label=\"Go to researcher profile for Mihaela Vorvoreanu\" data-bi-type=\"byline author\" data-bi-cN=\"Mihaela Vorvoreanu\">Mihaela Vorvoreanu<\/a> and Kathy Walker","formattedDate":"February 1, 2022","formattedExcerpt":"Inflated expectations around the capabilities of AI technologies may lead people to believe that computers can\u2019t be wrong. The truth is AI failures are not a matter of if but when. AI is a human endeavor that combines information about people and the physical world&hellip;","locale":{"slug":"en_us","name":"English","native":"","english":"English"},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/815857"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/users\/39507"}],"replies":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/comments?post=815857"}],"version-history":[{"count":63,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/815857\/revisions"}],"predecessor-version":[{"id":894516,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/815857\/revisions\/894516"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/815860"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=815857"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/categories?post=815857"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/tags?post=815857"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=815857"},{"taxonomy":"msr-region","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-region?post=815857"},{"taxonomy":"msr-event-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event-type?post=815857"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=815857"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=815857"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=815857"},{"taxonomy":"msr-promo-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-promo-type?post=815857"},{"taxonomy":"msr-podcast-series","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-podcast-series?post=815857"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}