ML Model Explainability

In the rapidly evolving world of machine learning (ML) and artificial intelligence (AI), developing models capable of delivering accurate predictions is no longer the sole objective. As organizations increasingly rely on data-driven decision-making, understanding the rationale behind a model’s predictions becomes paramount. The growing need for explainability in ML models stems from ethical, regulatory, and practical concerns, and it is here that the concept of Explainable AI (XAI) comes into play.

This chapter delves into the intricacies of Explainable ML models, a critical component in the MLOps landscape, with a focus on their implementation in the Google Cloud ecosystem. Although a comprehensive exploration of XAI techniques and tools is beyond this chapter’s scope, we aim to equip you with the knowledge and skills to build transparent, interpretable, and accountable ML models using the Explainable ML tools available on GCP.

The following...

What is Explainable AI and why is it important for MLOps practitioners?

XAI refers to methods and techniques that are used in the domain of AI that aim to make the decision-making processes of AI models transparent, interpretable, and understandable to humans. Instead of acting as black boxes where input data goes in and a decision or prediction comes out without clarity on how the decision was reached, XAI seeks to provide insights into the inner workings of models. This transparency allows users, developers, and stakeholders to trust and validate the system’s decisions, ensuring they align with ethical, legal, and practical considerations.

As ML continues to advance and its applications permeate various industries, the need for transparent and interpretable models has become a pressing concern. XAI aims to address this by developing techniques for understanding, interpreting, and explaining ML models. For MLOps practitioners working with Google Cloud, incorporating XAI...

Explainable AI techniques

Different techniques are available to cater to various types of data, including tabular, image, and text data. Each data type presents its own set of challenges and complexities, requiring tailored methods to provide meaningful insights into the decision-making processes of ML models. This subsection will list various XAI techniques applicable to tabular, image, and text data. The following section will dive into the ones available as out-of-the-box features in Google Cloud.

Global versus local explainability

Explainability can be categorized into two categories: local and global explainability. These terms are sometimes referred to as local and global feature importance:

• Global explainability focuses on the overall impact of a feature on the model. This is usually obtained by calculating the average feature attribution values over the entire dataset. A feature with a high absolute value indicates that it significantly influenced the model’...

Explainable AI features available in Google Cloud Vertex AI

Google Cloud Vertex AI offers a suite of tools and options tailored to make AI systems more understandable. This section delves into the various XAI options available in Vertex AI, showcasing how this platform is advancing the frontier of transparent ML.

Broadly, the XAI options available in Vertex AI can be divided into two types:

• Feature-based: Feature attributions refer to the degree to which each feature in a model contributes to the predictions for a specific instance. When making a prediction request, you receive the predicted values that are generated by your model. However, when requesting an explanation, you receive not only the predictions but also the feature attribution information.

It is important to note that feature attributions are primarily applicable to tabular data but also include built-in visualization capabilities for image data. This makes it easier to understand and interpret the...

Summary

In this chapter, we delved into the world of XAI and its relevance in modern MLOps. We discussed how XAI aids in building trust, ensuring regulatory compliance, debugging and improving models, and addressing ethical considerations.

We explored different explanation techniques for various types of data, including tabular, image, and text data. Techniques such as LIME, SHAP, permutation feature importance, and others were discussed for tabular data. For image data, methods such as Integrated Gradients and XRAI were explained, while text-specific LIME was presented for text data.

This chapter also provided an overview of the XAI features available in GCP, including both feature-based and example-based explanations.

At this point, you should have gained a good understanding of XAI, its importance, various techniques, and practical applications in the context of Vertex AI. As the field of AI continues to evolve, the role of XAI in creating transparent, trustworthy, and...

References

https://cloud.google.com/vertex-ai/docs/explainable-ai/overview

Munn, Michael; Pitman, David. Explainable AI for Practitioners. O’Reilly Media.