Which aspect of data is generally impacted by human biases?

Data accuracy is often impacted by human biases because biases can lead to errors in data collection, labeling, and interpretation. Human biases may cause selective reporting, where particular data points are emphasized while others are downplayed or ignored, ultimately distorting the true representation of the dataset. For example, if data is collected in a way that reflects societal biases—such as demographic representation—the resulting dataset may not accurately depict reality. This can lead to flawed conclusions or models built on this biased data, reinforcing those biases further.

It's essential to recognize that the accuracy of the data is crucial for reliable machine learning models. If the data is inaccurate due to human biases, it will adversely affect not just the training of the models but also their performance in real-world applications. Ensuring high data accuracy is vital for producing effective and fair machine learning outcomes.

In contrast, while data schema, preparation methods, and retention policies can be influenced by human decisions and practices, they do not directly reflect the inherent quality of the data itself in the same way that data accuracy does.