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Eliminating Bias in the Deployment of Machine Learning

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Webinar outline

The primary source of bias in machine learning is not in the algorithms deployed, but rather the data used as input to build the predictive models.  In this talk we will discuss why this is a huge problem and what to do about it. Different sources of bias will be identified along with possible solutions for remedying the situation when deploying machine learning.  We will also speak about the importance of transparency when using machine learning to predict outcomes that impact critical decisions.

Key takeaways:

  • Learn why most predictive models are biased.
  • Learn about the sources of bias in predictive models. 
  • Learn how to reduce the negative impact of potential bias in predictive models.


Stephen Brobst – CTO | Teradata

Stephen Brobst is the Chief Technology Officer for Teradata Corporation. Stephen performed his graduate work in Computer Science at the Massachusetts Institute of Technology where his Masters and PhD research focused on high-performance parallel processing. He also completed an MBA with joint course and thesis work at the Harvard Business School and the MIT Sloan School of Management.

Stephen is a TDWI Fellow and has been on the faculty of The Data Warehousing Institute since 1996. During Barack Obama’s first term he was also appointed to the Presidential Council of Advisors on Science and Technology (PCAST) in the working group on Networking and Information Technology Research and Development (NITRD) where he worked on development of the Big Data strategy for the US government. In 2014 he was ranked by ExecRank as the #4 CTO in the United States (behind the CTOs from Amazon.com, Tesla Motors, and Intel) out of a pool of 10,000+ CTOs.

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