
Clinicians, care managers, administrators, and executives all want to harness AI to improve clinical outcomes and drive healthcare efficiencies. Unfortunately, historical data, which is the fuel of modern AI systems, is often tainted with unintentional biases. How can we use this data without cementing these biases into new AI systems?
Join us for a live webinar with Duncan Renfrow-Symon and Cory Kind, Customer-Facing Data Scientists at DataRobot, as they discuss how unintentional biases in historical healthcare data have affected provider/payer services and outcomes for some patients.
They’ll review examples of biases in the data supporting major studies, and in the algorithms used to determine risk. You will also see how the functionality within the DataRobot platform can be used for the critical task of detecting and mitigating these biases when time, resources and, most importantly, lives are on the line.
During this webinar, you will learn:
- The sources of AI bias encountered in healthcare data
- How to automatically identify potential biases in your own data
- How to apply a metric that captures and mitigates the biases you are trying to avoid
Speakers

Customer Facing Data Scientist, DataRobot

Customer Facing Data Scientist, DataRobot
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DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely – it's almost like magic!
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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.
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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.
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DataRobot allows us to understand the data that’s being fed into our models without blindly feeding whatever we get into our system. DataRobot makes my team very effective.
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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.