Before we start, a fair warning is that you will not be learning how to write Python code to calculate a numeric score for computational bias in datasets. The primary focus of this chapter is to help you learn how to fetch real-world datasets from the Kaggle website and use observation to spot biases in data. There will be some coding to calculate the fairness or balance in the datasets.
For example, we will compute the word counts per record and the misspelled words in the text datasets.
You may think all biases are the same, but it helps to break them into three distinct categories. The bias categories’ differences can be subtle when first reading about data biases. One method to help distinguish the differences is to think about how you could remove or reduce the error in AI forecasting. For example, computational biases can be resolved by changing the datasets, while systemic biases can be fixed by changing the deployment and access strategy of...