Explainable AI (XAI)
Set of methods to make 'black box' model decisions understandable to humans, building trust and enabling auditing.
Key Concepts
Transparency
The ability to understand how an AI system makes decisions.
Interpretability
The ability to understand the meaning of the features that an AI system uses to make decisions.
Accountability
The ability to hold AI systems accountable for their actions.
Detailed Explanation
Explainable AI (XAI) is a field of artificial intelligence that is concerned with developing methods for making AI systems more transparent and understandable to humans. As AI systems become more powerful and autonomous, it is increasingly important to be able to understand how they make decisions.
XAI is a rapidly growing field, and there is a growing body of research on the topic. Some of the key challenges that are posed by XAI include:
- The Black Box Problem: Many AI systems are "black boxes," which means that it is difficult to understand how they make decisions. This is because they are often very complex.
- The Trade-off between Accuracy and Explainability: There is often a trade-off between the accuracy of an AI system and its explainability. This is because more accurate AI systems are often more complex, which makes them more difficult to understand.
Real-World Examples & Use Cases
Healthcare
XAI can be used to help doctors to understand why an AI system has made a particular diagnosis.
Finance
XAI can be used to help financial analysts to understand why an AI system has made a particular investment recommendation.
Criminal Justice
XAI can be used to help judges to understand why an AI system has made a particular sentencing recommendation.