In recent years, there has been a growing interest in developing machines that can make decisions without being too complex or unpredictable. This is because many real-world problems require the ability to explain how a machine arrived at a decision, especially when lives are involved. Explainable AI (XAI) is an emerging field that focuses on creating models that can interpret and explain their own decisions.
Context
In this article, we explore XAI in depth, discussing the challenges of working with deep learning models and how they can be made more interpretable. We examine three different approaches to XAI – model-specific explanations, model-agnostic explanations, and hybrid models that combine both. Finally, we discuss the importance of evaluating these models using real-world data.
Model-Specific Explanations
These models provide explanations specific to a particular deep learning model, such as how it interprets certain features or how it processes input data. One example is SHAP (SHapley Additive exPlanations), which assigns credit to individual input features for a model’s predictions.
Model-Agnostic Explanations
These models provide explanations that are not specific to any particular deep learning model, but instead offer general insights into how any deep learning model works. One example is LIME (Local Interpretable Model-agnostic Explanations), which generates an interpretable model locally around a specific instance.
Hybrid Models
These models combine both model-specific and model-agnostic explanations to provide a more comprehensive understanding of how a deep learning model works. One example is TreeExplainer, which uses decision trees to generate explanations for any deep learning model.
Conclusion
In summary, XAI is an important area of research that seeks to make deep learning models more interpretable and understandable. By using different approaches to explanation, such as model-specific and model-agnostic explanations, we can gain a better understanding of how these complex models work and why they arrive at certain decisions. As the use of deep learning models continues to grow in various industries, the need for XAI will only increase. Therefore, it is crucial that we continue to develop and improve these techniques to ensure that these models are used responsibly and ethically.
