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How Algorithmic Bias in ML Affects Marketing | Utilizing AI 3×23

As machine learning is used to market and sell, we must consider how biases in models and data can impact society. Arizona State University Professor Katina Michael joins Frederic Van Haren and Stephen Foskett to discuss the many ways in which algorithms are skewed. Even a perfect model will produce biased answers when fed input data with inherent biases. How can we test and correct this? Awareness is important, but companies and governments should take active interest in detecting bias in models and data.

Three Questions

  1. Frederic: When will AI be able to reliably detect when a person is lying?
  2. Stephen: Is it possible to create a truly unbiased AI?
  3. Tom Hollingsworth of Gestalt IT: Can AI ever recognize that it is biased and learn how to overcome it?


Katina Michael, Professor in the School for the Future of Innovation in Society and School of Computing and Augmented Intelligence at Arizona State University. Read here paper here in the Journal of Business Research. You can find more about her at


  • Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic on or on Twitter at @FredericVHaren.
  • Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day . Find Stephen’s writing at and on Twitter at @SFoskett

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About the author

Stephen Foskett

Stephen Foskett is an active participant in the world of enterprise information technology, currently focusing on enterprise storage, server virtualization, networking, and cloud computing. He organizes the popular Tech Field Day event series for Gestalt IT and runs Foskett Services. A long-time voice in the storage industry, Stephen has authored numerous articles for industry publications, and is a popular presenter at industry events. He can be found online at,, and on Twitter at @SFoskett.

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