Title: Understanding Interpretable and Explainable AI Techniques
Recent advancements in machine learning (ML) have led to the use of ML models in various fields to enhance performance and reduce human labor. ML models are increasingly employed in critical industries like medical diagnostics and credit card fraud detection. To optimize these models and address concerns regarding bias and other issues, it is essential to comprehend how they make predictions. This is where Interpretable (IAI) and Explainable (XAI) Artificial Intelligence techniques come into play. This article explores the differences between IAI and XAI models and their significance.
1. Interpretable Machine Learning:
Interpretable AI models can be easily understood by humans, without additional tools or methods. They provide their own explanation. For example, decision trees and linear regression models are interpretable. However, as models become more complex, understanding them becomes challenging.
2. Explainable Machine Learning:
Explainable AI models, also known as black-box models, have internal workings too complex for human understanding. Additional methods are required to comprehend these models. For instance, Random Forest Classifiers and neural network-based models fall under this category. Researchers employ various methods like partial dependence plots and SHapley Additive exPlanations (SHAP) to gain insights into model behavior.
Significance of Distinguishing Between Interpretability and Explainability:
It is crucial to distinguish between the interpretability and explainability of ML models. Some models are easier to interpret than others. While interpretable models are simpler and have lower accuracy, explainable models provide high accuracy at the expense of interpretability. Therefore, businesses must prioritize either interpretability or explainability based on project requirements.
In the rapidly evolving field of AI, understanding the differences between interpretable and explainable AI techniques is vital. Interpretable models can be easily understood by humans, while explainable models require additional methods to comprehend. By considering the needs of a specific project, organizations can choose the appropriate AI technique. It is important to stay informed about the latest AI research and developments to make informed decisions in this dynamic field.