What does bias in AI typically lead to?

Bias in AI typically leads to systematic errors and unfair outcomes because it reflects preconceived notions or unequal representation within the training data that an AI model learns from. When bias is present, certain groups or features may be overrepresented or underrepresented, causing the model to make decisions that are not equitable or just. This can manifest in various fields, such as hiring algorithms that favor one demographic over another, or facial recognition technologies that perform poorly on certain ethnic groups.

The consequences of such bias are significant, as they can perpetuate existing inequalities and lead to mistrust in AI systems. In real-world applications, this means that AI can inadvertently reinforce stereotypes or provide services that are less accessible or less accurate for marginalized groups.

The other options do not align with the impacts of bias in AI. While improved model accuracy may be a goal, bias does not contribute positively to this aspect; instead, it can skew predictions and outcomes. Similarly, bias does not inherently contribute to higher computational efficiency or increased data privacy, which are unrelated dimensions of AI development. Thus, the link between bias and systematic errors alongside unfair outcomes is what makes that choice the most accurate representation of the challenges posed by bias in AI.