Explainable AI, or XAI, is a branch of artificial intelligence that focuses on making the decisions made by AI systems transparent and understandable to humans. This is particularly important in fields where the consequences of AI decisions can be significant, such as healthcare, finance, and legal systems.
Key Concepts
- Model Interpretability: The ability to understand how an AI model makes decisions.
- Fairness: Ensuring that AI systems do not discriminate against certain groups of people.
- Robustness: The ability of an AI system to perform well under various conditions.
Why is XAI Important?
- Trust: XAI helps build trust between humans and AI systems.
- Regulatory Compliance: Many industries are regulated, and XAI can help ensure compliance with these regulations.
- Human-AI Collaboration: XAI enables humans to work more effectively with AI systems.
Example
Imagine a bank using AI to make credit decisions. Without XAI, the bank might not be able to explain why a particular loan application was denied. With XAI, the bank can provide insights into the factors that influenced the decision, such as credit score, income, and debt-to-income ratio.
Further Reading
For more information on Explainable AI, check out our Introduction to AI.
Explainable AI