What is explainability in machine learning?
An example of an AI chatbot conversation about explainability in machine learning.
What is explainability in machine learning?
Explainability in machine learning refers to the methods and techniques used to make the outputs of models understandable to humans.
Why is explainability important in AI?
It's crucial for trust, accountability, and compliance, especially in sensitive areas like healthcare and finance.
Can you give examples of explainable AI techniques?
Sure! Techniques include LIME, SHAP, and decision trees, which help clarify how models make decisions.
What are the challenges in achieving explainability?
Challenges include model complexity, the trade-off between accuracy and interpretability, and varying stakeholder needs.
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