Black Box in AI refers to a system or model whose internal workings are difficult or impossible to understand, even though you can see the input and the output. You know what the system does, but not how it makes its decisions.
Real-world examples:
- Deep learning models: A neural network might correctly identify diseases in medical scans, but doctors may not fully understand why it flagged certain images.
- Loan approval systems: An AI might approve or deny a loan application, but its decision process could be unclear to both users and developers.
Black box AI raises concerns about transparency and trust, which is why methods like Explainable AI (XAI) are being developed to make these systems more interpretable.