What is Explainable AI – Explainable AI
This last chapter of the book covers explainable AI. We will start with understanding what is explainable AI, its need, how explainable AI works on Vertex AI (for image and tabular data) and how to get the explanations from the deployed model.
In this chapter, we will discuss the following topics:
- What is Explainable AI
- Need of Explainable AI
- XAI on Vertex AI
- Data for Explainable AI exercise
- Model training for image data
- Image classification model deployment
- Explanations for image classification
- Tabular classification model deployment
- Explanations for tabular data
- Deletion of resources
- Limitations of Explainable AI
By the end of this chapter, you will have a good idea about explainable AI and will know how to get the explanations from the deployed model in Vertex AI.
Explainable AI (XAI) is a subfield of Artificial Intelligence (AI) that focuses on developing methods and strategies for using AI in a way that makes the outcomes of the solution understandable to human specialists. The mission of XAI is to ensure that AI systems be open and honest about not just the function they perform but also the purpose they serve. Interpretability is the broader umbrella under AI which includes explainable AI as one of its subcategories. Users can grasp what a model is learning, the additional information it must provide, and the reasoning behind its judgments concerning the problem that exists in the real world that we are seeking to solve, thanks to the model’s interpretability.
Explainable AI is one of the core ideas that define trust in AI systems (along with accountability, reproducibility, lack of machine bias, and resiliency). The aim and ambition shared by data scientists and machine learning technologists is the development of AI that is explainable.