Interpretability
Definition
Interpretability refers to the ability to understand and explain how AI systems make decisions or predictions.
Explain Like I'm 5
Imagine you have a robot friend who helps you pick what game to play. Interpretability is like asking the robot how it chooses the game so you can understand why it picked that one.
Digging Deeper
Interpretability in AI is crucial for building trust in automated systems. It involves making complex models understandable to humans by providing insights into the factors influencing their decisions. Techniques such as feature importance, model explanation, and attention mechanisms are used to enhance interpretability. For example, in healthcare, interpretable AI models can help doctors understand why a certain diagnosis was made, leading to better decision-making.
that is, in understanding the inner workings of AI systems— before models reach an overwhelming level of power.**
Applications
- Healthcare: Interpretable AI models can assist doctors in diagnosing diseases by explaining the rationale behind their predictions.
- Finance: Interpretable models are used in credit scoring to provide explanations for why a loan application was approved or denied.
- Autonomous vehicles: Understanding how self-driving cars make decisions can improve safety and trust among passengers.
- Legal: Interpretable AI can provide explanations for legal decisions, helping lawyers and judges understand the reasoning behind them.
- Customer service: Chatbots with interpretability features can provide explanations for their responses, improving user experience.