Why is AI interpretability important?

Why is AI interpretability important?

AI interpretability is crucial because it helps users understand how AI systems make decisions, ensuring trust and accountability.

What are the risks of using non-interpretable AI?

Non-interpretable AI can lead to biased decisions, lack of accountability, and difficulty in troubleshooting errors.

Can you give examples of interpretable AI models?

Sure! Examples include decision trees, linear regression, and rule-based systems, which provide clear decision-making processes.

How does interpretability affect AI ethics?

Interpretability is key to AI ethics as it promotes transparency, allowing stakeholders to assess fairness and mitigate biases.

What methods are used to improve AI interpretability?

Methods include feature importance analysis, LIME, SHAP values, and using simpler models that are inherently interpretable.