{"id":941334,"date":"2023-05-17T12:12:35","date_gmt":"2023-05-17T19:12:35","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/"},"modified":"2024-09-23T17:21:39","modified_gmt":"2024-09-24T00:21:39","slug":"responsible-ai-maturity-model","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/responsible-ai-maturity-model\/","title":{"rendered":"Responsible AI Maturity Model"},"content":{"rendered":"<p><strong>The Responsible AI Maturity Model (RAI MM) is a framework to help organizations plan how to achieve RAI maturity.<\/strong><\/p>\n<p>Organizations can use the RAI MM to:<\/p>\n<ul>\n<li>Plan their RAI strategy & programs (policy, processes, infrastructure)<\/li>\n<li>Help their AI product teams understand the sociotechnical nature of RAI and engage in the cross-discipline collaborations that are key to RAI<\/li>\n<li>Check on their RAI progress<\/li>\n<\/ul>\n<p>The RAI MM is not just a paper to read &#8211; it is a tool to use in strategic planning workshops. Contact the team if you need help with running a workshop &#8211; or use the materials below:<\/p>\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/05\/RAI-MM-Exec-Summary-PUBLISHED-Sept-23.pdf\">Download executive summary & FAQ<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/p>\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/05\/RAI-MM-for-PDF-printing-PUBLISHED-May-17.pdf\">Download the RAI MM<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/p>\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/05\/Introducing-the-RAIMM-Workshop-.pptx\">Download the RAI MM workshop facilitator deck<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/p>\n<p>&nbsp;<\/p>\n<p>The RAI MM contains 24 empirically derived dimensions that are key to an organization\u2019s RAI maturity. The dimensions and their levels are based on interviews and focus groups with over 90 RAI specialists (e.g., RAI champs, MSR experts) and AI practitioners (e.g., user experience (UX) researchers, UX designers, data scientists). Each dimension has five levels, going from low (Level 1: Latent) to high (Level 5: Leading) maturity. The dimensions are organized into three main categories:<\/p>\n<ul>\n<li>Organizational Foundations<\/li>\n<li>Team Approach<\/li>\n<li>RAI Practice<\/li>\n<\/ul>\n<p>Engage key stakeholders from different business units in a RAI strategic planning workshop. Follow these steps:<\/p>\n<ol>\n<li>Together, select a few RAI maturity dimension you want to focus in a given time frame (e.g., the next 6 months).<\/li>\n<li>For each selected dimension, decide what maturity level you want to aim for.<\/li>\n<li>Make a list of action items needed to reach the desired maturity level for each selected dimension.<\/li>\n<\/ol>\n<p>We welcome <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/forms.office.com\/r\/4ThLMPkEjU\" target=\"_blank\" rel=\"noopener\">your feedback<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>!<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Responsible AI Maturity Model (RAI MM) is a framework to help organizations plan how to achieve RAI maturity. Organizations can use the RAI MM to: Plan their RAI strategy & programs (policy, processes, infrastructure) Help their AI product teams understand the sociotechnical nature of RAI and engage in the cross-discipline collaborations that are key [&hellip;]<\/p>\n","protected":false},"featured_media":941364,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193718],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[258421],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-941334","msr-research-item","type-msr-research-item","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us","msr-field-of-study-responsible-ai"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2023-5-17","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"MSR-TR-2023-26","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"Microsoft","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/05\/RAI-MM-for-PDF-printing-PUBLISHED-May-17.pdf","id":"957555","title":"rai-mm-for-pdf-printing-published-may-17","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":1087584,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/RAI-MM-Exec-Summary-PUBLISHED-Sept-23.pdf"},{"id":1087563,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/Introducing-the-RAIMM-Workshop-.pptx"},{"id":1087560,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/Introducing-the-RAIMM-Workshop-.pptx"},{"id":957555,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/07\/RAI-MM-for-PDF-printing-PUBLISHED-May-17.pdf"},{"id":957549,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/07\/RAI-MM-Exec-Summary-for-PDF-printing-July-26.pdf"},{"id":941337,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/05\/RAI_Maturity_Model_Aether_Microsoft_whitepaper.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"Mihaela Vorvoreanu","user_id":36804,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Mihaela Vorvoreanu"},{"type":"text","value":"Amy Heger","user_id":0,"rest_url":false},{"type":"text","value":"Samir Passi","user_id":0,"rest_url":false},{"type":"text","value":"Shipi Dhanorkar","user_id":0,"rest_url":false},{"type":"text","value":"Zoe Kahn","user_id":0,"rest_url":false},{"type":"text","value":"Ruotong Wang","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[],"publication":[],"video":[],"download":[],"msr_publication_type":"techreport","related_content":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/941334"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":7,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/941334\/revisions"}],"predecessor-version":[{"id":1087587,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/941334\/revisions\/1087587"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/941364"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=941334"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=941334"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=941334"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=941334"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=941334"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=941334"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=941334"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=941334"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=941334"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=941334"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=941334"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=941334"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=941334"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=941334"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=941334"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=941334"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}