What is the primary benefit of using 'active learning' as a data labeling strategy when seeking to optimize both labeling costs and model accuracy?

The primary benefit of active learning is the systematic reduction of data labeling costs while maximizing model performance by ensuring that human experts only label the most informative data points. In a standard machine learning workflow, data is often labeled randomly, which frequently results in the human labeling redundant or easy examples that provide little new knowledge to th....