model-interpretability

Vocabulary Word

Definition

'Model-interpretability' refers to the clarity of how a machine learning model works. It's like seeing inside an engine to know how all the parts work together to make the car go.

Examples in Different Contexts

In explainable AI, 'model interpretability' means making the model's decision-making process understandable to humans. An AI ethicist might say, 'Improving model interpretability is crucial for building trust in AI systems by making their decisions transparent.'

Practice Scenarios

Tech

Scenario:

The precision of our AI model has improved. But the development team is struggling to explain why the AI is recognizing these patterns.

Response:

Through enhancing our model-interpretability, we can unravel the reasoning behind the AI's pattern recognition.

Product

Scenario:

The new software update has integrated AI functionalities. We now need to ensure our client base can grasp the workings and benefits.

Response:

By focusing on model-interpretability, we'll be able to explain our AI feature better to our clients.