What is the consequence of machine learning bias?

The consequence of machine learning bias is that it leads to inconsistent and unfair outcomes. When a machine learning model is trained on biased data, it can perpetuate or even exacerbate existing inequalities in the data it processes. For instance, if the training data lacks diversity or includes historical prejudices, the model may favor certain groups over others, leading to outcomes that systematically disadvantage these underrepresented groups.

Inconsistent outcomes can manifest in a variety of ways, such as skewed predictions, misclassifications, or unfair treatment in applications like hiring algorithms, loan approvals, or law enforcement profiling. This lack of fairness not only damages the model's credibility but can also have real-world implications by reinforcing social biases and inequalities. Therefore, recognizing and addressing machine learning bias is crucial for developing ethical and effective AI applications.