Explainable AI explained! | #1 Introduction

DeepFindr

7 Feb 202106:52

Summary

TLDRIn this new series, 'Explainable AI Explained,' the focus is on understanding machine learning models using explainable AI techniques. The series covers methods like identifying important input features, ensuring model robustness, and providing human-understandable explanations. The complexity of AI systems, often referred to as black boxes, necessitates these techniques, especially in critical areas like healthcare and autonomous driving. The series will explore both interpretable models and post-hoc methods like LIME and SHAP, as well as counterfactual explanations and layer-wise relevance propagation, offering practical examples and insights from the field.

Takeaways

📘 The series 'Explainable AI Explained' will cover how to better understand machine learning models using various methods from the explainable AI research field.
🤖 Explainable AI is crucial for understanding what machine learning models are learning, which parts of the input are most important, and ensuring model robustness.
🔍 The complexity of AI systems has led to them being seen as 'black boxes,' making it challenging even for experts to understand their inner workings.
🚗 Explainable AI is especially important in safety-critical areas like autonomous driving and healthcare, where understanding model decisions is essential.
📊 The field of explainable AI, also known as interpretable machine learning, has grown in interest, as seen in trends and research activity.
⚖️ A trade-off often exists between simple, interpretable models and complex models that provide better performance but are harder to understand.
🧠 The series will explore different explainable AI methods, including model-based and post hoc approaches, with a focus on methods like LIME and SHAP.
🔗 Methods in explainable AI can be categorized as either model-agnostic, applicable to any model, or model-specific, designed for specific models.
🌐 The series will also cover the difference between global and local approaches in explainable AI, focusing on explaining either the entire model or individual predictions.
📚 The content is largely based on the book 'Interpretable Machine Learning' by Christoph Molnar, which provides an extensive overview of explainable AI methods.

Q & A

What is the main focus of the video series 'Explainable AI Explained'?
-The main focus of the series is to explain and discuss methods for understanding machine learning models, particularly through the research field of Explainable AI (XAI).
Why is Explainable AI important in fields like autonomous driving and healthcare?
-Explainable AI is crucial in these fields because they involve safety and health-critical environments where understanding and validating AI decisions is essential for trust and transparency.
What is meant by AI models being referred to as 'black boxes'?
-AI models are referred to as 'black boxes' because their complexity makes them incomprehensible, even to experts, making it difficult to understand how they arrive at specific decisions.
What are the two main types of machine learning models discussed in terms of interpretability?
-The two main types are simple linear models, which are easy for humans to interpret but may not perform well on complex problems, and highly non-linear models, which perform better but are too complex for humans to understand.
What is the difference between model-based and post hoc explainability methods?
-Model-based methods ensure that the machine learning algorithm itself is interpretable, while post hoc methods provide human-understandable explanations after the model has been trained, often without requiring access to the model's internal workings.
What is the difference between global and local explainability approaches?
-Global explainability aims to explain the entire model, while local explainability focuses on understanding specific predictions or parts of the model, especially in areas with complex decision boundaries.
What are model-agnostic and model-specific explainability methods?
-Model-agnostic methods can be applied to any type of machine learning model, whereas model-specific methods are designed for a particular model type, such as neural networks.
What types of explanations can Explainable AI methods provide?
-Explainable AI methods can provide various types of explanations, including correlation plots, feature importance summaries, data points that clarify the model's behavior, and simple surrogate models that approximate complex ones.
What is a surrogate model in the context of Explainable AI?
-A surrogate model is a simpler model built around a complex machine learning model to provide explanations that are easier for humans to understand.
Which specific Explainable AI methods will be discussed in the video series?
-The series will cover interpretable machine learning models, LIME, SHAP, counterfactual explanations, adversarial attacks, and layer-wise relevance propagation, with a focus on their application and effectiveness.

Outlines

00:00

🤖 Introduction to Explainable AI

This paragraph introduces a new series on 'Explainable AI Explained,' aiming to demystify machine learning models using techniques from the explainable AI research field. It discusses the importance of understanding AI models, especially in critical sectors like healthcare and autonomous driving, and the challenges posed by the complexity of deep learning systems. The paragraph also highlights the distinction between model-based and post hoc methods, and the importance of model interpretability for both data scientists and end users. It sets the stage for a deeper dive into various explainable AI methods and touches upon the growing interest in this field as evidenced by Google Trends data.

05:01

📚 Exploring Explainable AI Methods and Terminology

The second paragraph delves into the specifics of explainable AI methods, differentiating between model-agnostic and model-specific approaches, as well as global and local explanation scopes. It introduces the concept of 'surrogate' models that simplify complex models for better understanding. The paragraph also emphasizes the variety of explanations that can be derived, such as correlation plots, feature importance, and counterfactual explanations. It mentions the 'awesome machine learning interpretability' GitHub project as a valuable resource and outlines the content for the upcoming videos in the series, including discussions on interpretable machine learning models, LIME, SHAP, counterfactual explanations, and adversarial attacks, concluding with a mention of causal reasoning as a higher degree of interpretability.

Mindmap

Keywords

💡Explainable AI

Explainable AI refers to the field of research focused on making machine learning models understandable and interpretable by humans. It is crucial for applications in safety-critical environments like healthcare and autonomous driving, where understanding model decisions is essential. In the video, the term is introduced as the main theme, emphasizing the need for transparency in AI systems to build trust and ensure proper functioning.

💡Machine Learning Models

Machine learning models are algorithms that learn from data and make predictions or decisions without being explicitly programmed. The script discusses the complexity of these models, which can become incomprehensible even to experts, hence the term 'black boxes.' The video aims to explore methods to demystify these models through explainable AI.

💡Deep Learning

Deep learning is a subset of machine learning that uses neural networks with many layers to learn and make decisions. The script mentions the progress in deep learning as a significant factor in the widespread use of AI systems, but also notes the complexity it introduces, making models harder to interpret.

💡Black Box

In the context of AI, a 'black box' refers to a system where the internal processes are not visible or understandable from the outside. The script uses this term to describe why machine learning models are often opaque, highlighting the need for explainable AI to make these processes transparent.

💡Interpretable Machine Learning

Interpretable machine learning is a branch of AI that focuses on creating models that are inherently understandable. The video mentions this as an alternative to complex models that are difficult to interpret, suggesting that some models can be designed to be more transparent from the outset.

💡Model Agnostic

Model agnostic methods in explainable AI are techniques that can be applied to any type of model, regardless of its architecture. The script differentiates these from model-specific methods, emphasizing the versatility of model agnostic approaches in providing explanations across different types of machine learning models.

💡Global and Local Approaches

Global approaches aim to explain the entire model, while local approaches focus on individual predictions or parts of the model. The script uses these terms to categorize explainable AI methods, with 'global' providing a broad understanding and 'local' offering insights into specific model decisions.

💡Feature Importance

Feature importance refers to the significance of different input features in making a prediction. The script mentions this as a type of explanation that models can provide, helping to understand which parts of the input data most influence the model's decisions.

💡Surrogate Models

Surrogate models are simple models built around complex ones to provide explanations for their behavior. The script describes these as a method of explainable AI, where the surrogate acts as an interpretable proxy to derive insights from the original, more complex model.

💡LIME

LIME stands for Local Interpretable Model-agnostic Explanations. It is a method highlighted in the script for providing local explanations for individual predictions. LIME works by creating a simple model that approximates the complex model's decision at a specific point in the input space.

💡Counterfactual Explanations

Counterfactual explanations provide insights into what would need to change in the input data for a different outcome. The script briefly mentions this method, indicating it as a way to understand the causal relationships within model decisions.

Highlights

Introduction to Explainable AI (XAI) and the importance of understanding machine learning models.

The growing complexity of AI systems, often referred to as 'black boxes', and the need for interpretability, especially in critical areas like healthcare and autonomous driving.

Overview of the field of Explainable AI, also known as Interpretable Machine Learning, which provides techniques to understand and validate machine learning models.

Introduction to the trade-off between simple linear models that are easy to interpret and complex non-linear models that provide better performance but are harder to understand.

Explanation of model-based vs. post hoc methods for making machine learning models interpretable.

Description of black box approaches (using only the relationship between inputs and outputs) and white box approaches (accessing model internals like gradients or weights).

Introduction to the concept of global vs. local explainability, explaining the entire model vs. individual predictions.

Discussion of how some methods are model-agnostic (can be applied to any model) while others are model-specific (designed for specific types of models).

Overview of different types of explanations produced by models, such as correlation plots, feature importance, and data points for understanding the model.

Introduction to surrogate models, which are simpler models built around complex ones to provide explanations.

Mention of the 'Interpretable Machine Learning' book by Christoph Molnar as a comprehensive resource on XAI methods.

Outline of the video series covering four XAI algorithms, starting with interpretable machine learning models.

Introduction to popular methods like LIME and SHAP, which will be covered in detail in the series.

Discussion of counterfactual explanations and adversarial attacks, which will be explored in the series.

Introduction to Layer-Wise Relevance Propagation, a method specifically designed for neural networks, which will be covered in the series.