Gartner® Innovation Insight for Bias Detection/Mitigation, Explainable AI and Interpretable AI

“Data and analytics leaders must understand responsible AI and the measurable elements of that hierarchy — bias detection and mitigation, explainability, and interpretability. Supporting these practices facilitates understanding, trust and performance accountability required by stakeholders.”

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According to Gartner, “Data and analytics leaders must consider many layers when planning for the responsible use of AI in the business. Some of these layers are conceptual and define the behavior of the organization when it comes to the use of AI. Other layers are practical and make organizations accountable for the models they employ. The overarching layer is responsible AI, beneath which are layers such as trustworthy AI and transparent AI”

This report provides businesses and AI developers with information and guidance to focus on key issues such as bias detection and prevention, explainability and interpretability. This helps to develop more equitable, reliable and user-friendly AI systems. According to the report Key Findings:

  • The growing use of artificial intelligence (AI) and machine learning (ML) has increased concerns about the use of these models in ways that impact individuals without them knowing how models work.
  • There is much discussion but little consensus about what constitutes explainable and interpretable AI among practitioners of AI.
  • A combination of bias detection/mitigation tools and explainable and/or interpretable AI tooling are an important part of what is needed to establish trust in AI.
  • “Black box” models such as those that use deep neural networks cannot be mathematically explained, but can be interpreted so that stakeholders know how they work and what to expect in terms of their predictions.

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Innovation Insight for Bias Detection/Mitigation, Explainable AI and Interpretable AI, Van Baker, Svetlana Sicular, Avivah Litan, Erick Brethenoux, 9 February 2022

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document.



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