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How to Start Tackling AI Bias

Part 2: Building Fair AI

The stories of bias in AI are everywhere: Amazon’s recruiting tool, Apple’s credit card limits, Google’s facial recognition, and dozens more. The quick solution is just to blame the algorithm and its designers. But it’s not a question of whether or not you have bias in your institution, because every organization does. Rather, the real question is how you plan to handle it.

In part two of this two-part podcast series, How to Start Tackling AI Bias, Jett Oristagio, Data Science & Product Lead of Trusted AI at DataRobot, will take a deeper dive into how to tackle AI bias.

Listen to this podcast to learn:

  • How machine learning can highlight the implicit bias of an institution and how AI is a new toolset to measure and change it
  • A practical plan that you can implement to improve your AI development and increase trust in your AI


Jett Oristaglio
Jett Oristaglio

Data Science and Product Lead

  • DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely – it's almost like magic!
    Omair Tariq
    Omair Tariq

    Data Analyst, Symphony Post Acute Network

  • I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.
    Oliver Rees
    Oliver Rees

    General Manager – Torque Data at Virgin Australia

  • At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.
    Akshay Tandon
    Akshay Tandon

    VP of Strategy Analytics, LendingTree

  • We know part of the science and the heavy lifting are intrinsic to the DataRobot technology. Prior to working with DataRobot, the modeling process was more hands-on. Now, the platform has optimized and automated many of the steps, while still leaving us in full control. Without DataRobot, we would need to add two full-time staffers to replace what DataRobot delivers.