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For Customer Insights Professionals

Evoke Trust With Explainable AI

Balance Transparency With Accuracy To Increase Stakeholder Adoption And Mitigate Risk

November 1, 2018

Why Read This Report

The promise of AI is rapidly materializing; however, not all AI is created equal. Some machine learning algorithms are transparent, allowing brands to see how they make decisions, while others are opaque, obscuring the inner logic of models. Still others are transparent yet so convoluted that the results are not easily explainable. This report highlights the evolving area of explainable AI and how customer insights (CI) pros should consider the tradeoffs in risk, accuracy, and applicability when they determine the level of explainability in AI.

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Table of Contents

  • Explaining The Inner Workings Of AI Is Not Black Or White
  • There Are Multiple Flavors Of Explainability In AI
  • Partner With Risk And Compliance Pros To Navigate Explainability
  • Recommendations

  • Prioritize Explainability To Drive Adoption
  • Supplemental Material
  • Related Research Documents

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