What is AI bias and how does it affect data?
AI bias refers to systematic errors in AI systems that can lead to unfair outcomes. It often arises from biased training data.
Can you give me an example of AI bias in real-world applications?
Sure! One example is facial recognition technology, which has shown higher error rates for people of color due to biased training datasets.
How can we mitigate AI bias in data?
Mitigating AI bias involves using diverse datasets, implementing fairness checks, and continuously monitoring AI systems for bias.
Is it possible to completely eliminate bias from AI?
While it's challenging to completely eliminate bias, we can significantly reduce it through careful design and ongoing evaluation.
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