How does AI bias affect explainability?

Can you explain how AI bias impacts the explainability of AI systems?

AI bias can obscure the reasoning behind decisions, making it harder to understand how outcomes are derived.

What are some examples of AI bias affecting real-world applications?

Examples include biased hiring algorithms that favor certain demographics or facial recognition systems that misidentify individuals.

How can we mitigate AI bias to improve explainability?

Mitigation strategies include diverse training data, regular audits, and transparency in algorithm design.

Is there a way to measure AI bias?

Yes, bias can be measured using fairness metrics, which assess how different groups are treated by the AI.

What role does user feedback play in reducing AI bias?

User feedback is crucial as it helps identify biases in AI outputs and informs adjustments to improve fairness.