Explanation
Imagine a doctor using a new diagnostic tool that makes predictions but doesn't explain how it arrived at them. Would you trust the diagnosis? Model interpretability is about making the 'black box' of AI models more transparent.
It's the degree to which humans can understand the cause-and-effect relationships a model has learned. It's about knowing why a model made a specific prediction or decision.
Think of it as shining a light inside the model, allowing us to see which factors influenced its output. This helps us to trust the model, identify potential biases, and improve its performance.
Without interpretability, AI models can be opaque and difficult to trust, especially in critical applications where transparency and accountability are paramount.
Examples
Consumer Example
Consider a loan application being rejected by an AI-powered system. With model interpretability, the applicant can understand the specific factors that led to the rejection, such as credit score or income level.
This allows them to take corrective action, improve their financial situation, and reapply with a better chance of success. It's about providing transparency and empowering individuals to understand the reasons behind AI-driven decisions.
Business Example
Imagine a marketing team using an AI model to predict which customers are most likely to churn. Model interpretability can reveal the specific factors driving churn, such as price sensitivity or dissatisfaction with customer service.
This allows the marketing team to tailor their retention efforts more effectively, addressing the root causes of churn and improving customer loyalty. It's about using AI to gain deeper insights into customer behaviour and drive business outcomes.