How does diversity in training data affect AI performance?

How does diversity in training data affect AI performance?

Diversity in training data is crucial as it helps AI models understand and represent various perspectives, reducing bias.

Can you give examples of how bias can occur in AI?

Sure! Bias can occur if the training data over-represents certain demographics, leading to skewed predictions or outputs.

What steps can be taken to ensure diverse training data?

To ensure diversity, one can curate datasets from multiple sources, include various demographics, and regularly audit the data.

Are there any tools to help with this process?

Yes, there are tools like Fairness Indicators and AI Fairness 360 that help assess and improve the fairness of AI models.

What are the consequences of not addressing diversity in AI?

Not addressing diversity can lead to biased AI systems, which may perpetuate stereotypes and cause harm to underrepresented groups.