How can we address bias in AI?

What are some common biases found in AI systems?

Common biases in AI include gender bias, racial bias, and socioeconomic bias, often stemming from biased training data.

How can we identify these biases in AI models?

Biases can be identified through audits, testing with diverse datasets, and analyzing model predictions for fairness.

What steps can be taken to mitigate bias in AI?

To mitigate bias, we can use diverse training data, implement fairness algorithms, and continuously monitor AI performance.

Are there any frameworks or guidelines for addressing bias in AI?

Yes, frameworks like the Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) provide guidelines for addressing bias.