What is AI bias in evaluation?

What is AI bias in evaluation?

AI bias in evaluation refers to systematic errors in AI systems that lead to unfair outcomes, often due to biased training data.

How does this bias affect decision-making?

Bias can lead to discriminatory practices in areas like hiring, lending, and law enforcement, impacting lives negatively.

What are some examples of AI bias in real-world applications?

Examples include biased facial recognition systems that misidentify people of color and algorithms that favor certain demographics in hiring.

How can we mitigate AI bias?

Mitigation strategies include using diverse training datasets, regular audits of AI systems, and implementing fairness guidelines.

Are there any regulations on AI bias?

Yes, various countries are developing regulations to address AI bias, focusing on transparency, accountability, and fairness.