How can we avoid bias in AI training data?

How can we avoid bias in AI training data?

To avoid bias, we should ensure diverse and representative datasets, and regularly audit them for fairness.

What are some common sources of bias in training data?

Common sources include historical biases, unrepresentative samples, and subjective labeling by humans.

Can you give examples of how bias has affected AI outcomes?

Yes, bias in facial recognition systems has led to higher error rates for people of color compared to white individuals.

What techniques can be used to mitigate bias during training?

Techniques include data augmentation, adversarial training, and using fairness constraints in model training.

Is there a way to measure bias in AI models?

Yes, metrics like demographic parity and equal opportunity can help assess bias in AI models.