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Building AI Explanation Capability for the AI-Powered Organization

This audio edition of the July 2022 research briefing explores how an organization can build confidence that its AI solutions are trustworthy.
No. XXII-7 Published: Jul 21, 2022 Topic(s): Data Monetization; Information Technologies
By Ida A. Someh, Barbara H. Wixom, and Cynthia (Dr. Boo) M. Beath
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Four characteristics of AI—unproven value, model opacity, model drift, and mindless application—make it challenging to get stakeholders to trust AI solutions. As a result, organizations that strive to become AI-powered adopt practices to produce AI solutions that are trustworthy. Over time, these practices build AI Explanation (AIX) capability: an emerging enterprise capability that arises from practices AI teams use to build stakeholder confidence in AI solutions. In this briefing, we first describe AIX capability and four sets of practices used to build it. We then draw on a case study about the AI journey of Microsoft to illustrate examples of the practices that company has leveraged.

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The July 2022 research briefing is read by co-author Ida Someh.

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© 2022 MIT Sloan Center for Information Systems Research, Someh, Wixom, and Beath. MIT CISR Research Briefings are published monthly to update the center’s patrons and sponsors on current research projects.

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About the Authors

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Ida A. Someh, Senior Lecturer in Business Information Systems, UQ Business School at The University of Queensland, Australia

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Cynthia (Dr. Boo) M. Beath, Professor Emerita, University of Texas at Austin

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