Building AI Explanation Capability for the AI-Powered Organization
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2022_0701_AIX_SomehWixomBeath.pdf
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504.04 KB
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Author(s)
Someh, Ida A.
Wixom, Barbara H.
Beath, Cynthia M.
Date Issued
July 21, 2022
Publisher
Cambridge, Mass.: Center for Information Systems Research, Sloan School of Management, Massachusetts Institute of Technology
Citation
Someh, Ida A., Barbara H. Wixom, and Cynthia M. Beath. “Building AI Explanation Capability for the AI-Powered Organization.” MIT CISR Research Briefing Volume XXII, no. 7 (July 2022). https://dspace.mit.edu/handle/1721.1/175005.
Series/Report no.
MIT CISR Research Briefing, Vol. XXII, No. 7
Abstract
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.
Subjects
Data monetization
Information Technologies
Data, AI, and Analytics
Terms of Use
CC BY-NC 4.0 (Attribution-NonCommercial 4.0 International)
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