The Importance of Understanding Bias in Machine Learning

Many of the benchmark data sets that are available for model training don't tend to match what we see in practice in medical data sets. Some of these problems are hard to address because the samples are small and cannot be made larger. A lot of what we do on the non-data side, thinking about how models themselves can become more robust when you have a certain baseline of data is thinking about what makes this model perform poorly.