How does bias affect machine learning outcomes?

Bias in machine learning decreases the accuracy of predictions and can skew the results in a way that misrepresents the underlying data. When a machine learning model is trained on biased data, it learns patterns that reflect those biases rather than the true characteristics of the entire dataset. This can result in systemic errors, where certain groups or outcomes are favored depending on the nature of the bias in the training data, leading to inaccurate predictions.

Bias can occur in various forms, such as selection bias, measurement bias, and confirmation bias. Each of these can distort the learning process, causing the model to generalize poorly to new data or to ignore relevant features entirely. This effect is particularly problematic in sensitive applications, such as hiring practices or credit scoring, where biased predictions can have significant real-world impacts.

In contrast, the other options do not accurately reflect the impact of bias in machine learning. Bias does not improve accuracy or enhance the diversity of a model; rather, it can reinforce existing disparities or inequalities within the data. Similarly, bias complicates the learning process rather than simplifying it, because addressing bias often requires careful consideration and additional steps during data collection and model training.