How can we mitigate bias in AI systems?

How can we mitigate bias in AI systems?

Mitigating bias in AI involves diverse data collection, algorithm transparency, and continuous monitoring.

What are some common sources of bias in AI?

Common sources include biased training data, societal stereotypes, and lack of diversity in development teams.

Can you give examples of biased AI outcomes?

Sure! Examples include biased hiring algorithms favoring certain demographics or facial recognition systems misidentifying people of color.

What steps can organizations take to ensure fairness?

Organizations can implement regular audits, involve diverse teams in development, and use fairness metrics to evaluate outcomes.