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.
