{"id":1017525,"date":"2024-03-21T16:05:00","date_gmt":"2024-03-21T23:05:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=1017525"},"modified":"2024-03-21T16:13:59","modified_gmt":"2024-03-21T23:13:59","slug":"appropriate-reliance-on-generative-ai-research-synthesis","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/appropriate-reliance-on-generative-ai-research-synthesis\/","title":{"rendered":"Appropriate reliance on Generative AI: Research synthesis"},"content":{"rendered":"<p>Appropriate reliance on AI happens when users accept correct AI outputs and reject incorrect ones. New complexities arise for fostering appropriate reliance on generative AI (GenAI) systems. GenAI systems pose several risks, despite often rivaling, and sometimes surpassing, human performance on many tasks. Inappropriate reliance \u2013 either under-reliance or overreliance \u2013 on GenAI can have negative consequences such as poor human+GenAI team performance and even product abandonment. Based on a review of ~50 papers from multiple research areas, this report provides an overview of the factors that affect overreliance on GenAI, the effectiveness of different mitigation strategies for overreliance on GenAI, and potential design strategies to facilitate appropriate reliance on GenAI.<\/p>\n<p>&nbsp;<\/p>\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/overreliance-on-ai-literature-review\/\" target=\"_blank\" rel=\"noopener\">See also our 2022 research synthesis on Overreliance on AI<\/a><\/p>\n<p><strong>Cite as:<\/strong> Samir Passi, Shipi Dhanorkar, & Mihaela Vorvoreanu. 2024.<em> Appropriate Reliance on Generative AI: Research Synthesis<\/em>. Microsoft Technical Report MSR-TR-2024-7. Microsoft Corporation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Appropriate reliance on AI happens when users accept correct AI outputs and reject incorrect ones. New complexities arise for fostering appropriate reliance on generative AI (GenAI) systems. GenAI systems pose several risks, despite often rivaling, and sometimes surpassing, human performance on many tasks. Inappropriate reliance \u2013 either under-reliance or overreliance \u2013 on GenAI can have [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"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-field-of-study":[246694],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-1017525","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us","msr-field-of-study-artificial-intelligence"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2024-3-21","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-2024-7","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\/2024\/03\/GenAI_AppropriateReliance_Published2024-3-21.pdf","id":"1017537","title":"genai_appropriatereliance_published2024-3-21","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":1017537,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/03\/GenAI_AppropriateReliance_Published2024-3-21.pdf"}],"msr-author-ordering":[{"type":"text","value":"Samir Passi","user_id":0,"rest_url":false},{"type":"text","value":"Shipi Dhanorkar","user_id":0,"rest_url":false},{"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"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[],"publication":[],"video":[],"download":[],"msr_publication_type":"techreport","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/1017525"}],"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":4,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/1017525\/revisions"}],"predecessor-version":[{"id":1017555,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/1017525\/revisions\/1017555"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=1017525"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=1017525"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=1017525"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=1017525"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=1017525"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=1017525"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=1017525"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=1017525"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=1017525"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=1017525"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=1017525"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=1017525"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=1017525"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=1017525"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=1017525"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}