Explainable AI (XAI) refers to artificial intelligence systems designed to be transparent about how they work and why they make certain decisions. The goal is to help humans understand and trust the AI by making its processes more accessible and less like a “black box”.
Real-world examples:
- Healthcare: Imagine an AI system recommends a treatment for a patient. With XAI, doctors can see why the system made that suggestion, such as highlighting specific symptoms, lab results, or medical history it considered.
- Loan approvals: If an AI denies someone a loan, XAI can explain the factors behind the decision, like income, credit score, or spending patterns. This helps ensure fairness and transparency.
- Self-driving cars: If a self-driving car suddenly brakes, XAI can provide insights, such as detecting a pedestrian or interpreting traffic signs.
Explainable AI is essential for building trust, ensuring fairness, and enabling humans to challenge or refine AI decisions when needed.