Building AI Explanation Capability For The AI-Powered Organization MIT CISR podcast

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Building AI Explanation Capability for the AI-Powered Organization

2+ y ago 14:34

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Indhold leveret af MIT CISR. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af MIT CISR eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

Ida Someh reads MIT CISR's July 2022 research briefing, which she co-authored with Barb Wixom and Cynthia Beath. See the text version and related content at https://cisr.mit.edu/publication/2022_0701_AIX_SomehWixomBeath. 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.

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85 episoder

#Business #MIT CISR

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Building AI Explanation Capability for the AI-Powered Organization

published 2+ y ago

Del

MP3•Episode hjem

Indhold leveret af MIT CISR. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af MIT CISR eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

Ida Someh reads MIT CISR's July 2022 research briefing, which she co-authored with Barb Wixom and Cynthia Beath. See the text version and related content at https://cisr.mit.edu/publication/2022_0701_AIX_SomehWixomBeath. 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.

… continue reading

85 episoder

#Business #MIT CISR

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