What does "Explainable AI (XAI)" mean?

Explainable AI (XAI) refers to techniques and methods in artificial intelligence that aim to make the decisions and outputs of AI models understandable and interpretable by humans.

Use Cases

Healthcare:

Explaining the reasons behind a medical diagnosis made by an AI model.

Finance:

Providing transparency in AI-driven decisions related to credit scoring or investment recommendations.

Legal:

Justifying AI recommendations in legal contexts, such as predicting case outcomes.

Importance

Transparency:

Builds trust by explaining how AI models arrive at decisions or recommendations.

Accountability:

Helps stakeholders understand and validate AI-driven decisions, reducing biases and errors.

Compliance:

Facilitates adherence to regulatory requirements that demand explanations for automated decisions.

Analogies

Explainable AI is like a detailed map that shows you the route taken by a navigation system. Instead of blindly following directions, you understand why certain turns are recommended, making you more confident in reaching your destination.

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