Understanding Bias in Machine Learning

June 24th, 2020

Artificial intelligence is advancing at an increasingly fast pace and may soon play an integral role in how our society functions in everyday life. As this development progresses, it's more important than ever to understand how the systems behind this technology work, and how they fail. This is a guest post for Scalable Path in which we dig into the complex issue of bias in Machine Learning with real-world examples, what causes it, and how we can address it moving forward.


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AUTHOR
I help companies through digital transformations by designing and developing systems that improve human decision making with data, cloud, software, and mathematics (Machine Learning, Operations Research, Statistics). I also enjoy coaching teams and publishing content on Smart Automation.

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