Using sentiment analysis API and want to know how the AI bias that gets in through the training set of data and other biases quantified. Any help would be appreciated.
There are several tools developed to deal with it: Fair Learn https://fairlearn.github.io/ Interpretability Toolkit https://learn.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability
In Fair Learn you can see how biased a ML model is after it has been trained with the data set and choose a maybe less accurate model which performs better with biases. The explainable ML models provide different correlation of inputs with outputs and combined with Fair Learn can give an idea of the health of the ML model.