Get Biases Machine Learning Background. Missing data and patients not identified by algorithms, sample size and underestimation, misclassification and measurement errors. Why do we care about societal bias in ml models?
There has been a growing interest in identifying the harmful biases in the machine learning. This doesn't solve the problem of cognitive bias in machine learning as a whole, but it opens the doors toward collaboration and innovation in this space. One key challenge is the presence of bias in the classifications and predictions of machine learning.
There has been a growing interest in identifying the harmful biases in the machine learning.
When building models, it's important to be aware of common human biases that. Bias in data can exist in many shapes and forms, some of which can lead to unfairness in different downstream learning tasks. Some biases have harmful consequences for some groups of patients and are unjust. Machine learning models can reflect the biases of organizational teams, of the designers in those teams, the data scientists who implement the models, and the data engineers that gather data.