What is AI bias in machine learning?

What is AI bias in machine learning?

AI bias in machine learning refers to systematic errors in algorithms that lead to unfair outcomes, often due to biased training data.

How does bias get into machine learning models?

Bias can enter models through biased data, flawed algorithms, or human prejudices during the data collection and labeling process.

What are the consequences of AI bias?

Consequences include discrimination in hiring, lending, law enforcement, and other critical areas, leading to social inequality.

How can we mitigate AI bias?

Mitigating AI bias involves using diverse datasets, implementing fairness algorithms, and regularly auditing models for bias.

Are there any real-world examples of AI bias?

Yes, notable examples include biased facial recognition systems and discriminatory hiring algorithms that favor certain demographics.