What is XAI? 

XAI or Explainable AI Techniques, are methods that enhance transparency in AI decision-making processes. They provide insights into how AI models arrive at specific outcomes, helping to interpret and validate their predictions.

How Explainable AI Techniques Work 

XAI analyzes AI model outputs to generate explanations that users can understand. They highlight influential features or data points that contribute to model decisions, promoting trust and usability.

Explainable AI Technique Benefits

• Transparency: Increases trust in AI systems by revealing the reasoning behind predictions or recommendations.
• Accountability: Facilitates compliance with regulations and ethical standards by providing justification for AI-driven decisions.
• Improves Debugging: Helps identify and correct biases or errors in AI models, enhancing overall reliability and fairness.

Use Cases for Explainable AI (XAI) Techniques

• Credit Scoring: Provide transparency into credit scoring decisions for fairness and accountability.
• Healthcare Diagnostics: Better AI-driven diagnostic decisions to aid healthcare professionals.
• ‍Legal Document Review: Enhance legal document analysis by revealing influencing factors.