Emerging Technologies
Explainable AI (XAI)
Definition
Explainable AI (XAI) refers to methods and techniques in artificial intelligence that enable human users to comprehend and trust the results and output created by machine learning algorithms. It aims to answer the question of "Why did the AI make that decision?".
Why It Matters
As AI models become more complex ("black boxes"), it becomes harder to understand how they work. XAI is crucial for building trust, ensuring fairness, and debugging models, especially in high-stakes domains like medicine and finance.
Contextual Example
An AI model denies a loan application. With XAI, the model could highlight the specific factors that led to the denial (e.g., "low credit score" and "high debt-to-income ratio"), making the decision transparent and auditable.
Common Misunderstandings
- There is often a trade-off between model performance and explainability. The most accurate models (like deep neural networks) are often the least transparent.
- XAI is a critical area of research for the responsible and ethical deployment of AI.