What Explainability Meaning, Applications & Example
The ability to understand and interpret the decision-making process of an AI system.
What is Explainability?
Explainability refers to the ability of an AI system to provide clear, understandable, and transparent reasons for its decisions or predictions. It is an essential aspect of responsible AI development, ensuring that users can trust and comprehend how and why AI models arrive at specific outcomes. Explainable AI (XAI) aims to bridge the gap between complex machine learning models and human understanding.
Types of Explainability
- Model Explainability: Making the inner workings of AI models transparent, such as revealing how a decision is made in a neural network or decision tree .
- Prediction Explainability: Providing clear, understandable reasons for individual predictions, such as highlighting important features or data points that influenced the outcome.
Applications of Explainability
- Healthcare: AI systems used for diagnosing diseases must explain their reasoning to clinicians to ensure trust and proper treatment decisions.
- Finance: AI models used for credit scoring need to be explainable to regulators and customers to ensure fairness and transparency .
- Legal: AI used in the legal industry, such as for sentencing or predictive policing, must provide explanations to avoid biases and ensure accountability.
Example of Explainability
A loan approval AI system might explain that a user’s low credit score was a major factor in its decision to deny the application, detailing how specific financial behaviors contributed to the outcome.