Explainable AI explained! | #1 Introduction
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
- π The series 'Explainable AI Explained' will cover how to better understand machine learning models using various methods from the explainable AI research field.
- π€ Explainable AI is crucial for understanding what machine learning models are learning, which parts of the input are most important, and ensuring model robustness.
- π 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.
- π Explainable AI is especially important in safety-critical areas like autonomous driving and healthcare, where understanding model decisions is essential.
- π The field of explainable AI, also known as interpretable machine learning, has grown in interest, as seen in trends and research activity.
- βοΈ A trade-off often exists between simple, interpretable models and complex models that provide better performance but are harder to understand.
- π§ The series will explore different explainable AI methods, including model-based and post hoc approaches, with a focus on methods like LIME and SHAP.
- π Methods in explainable AI can be categorized as either model-agnostic, applicable to any model, or model-specific, designed for specific models.
- π 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.
- π 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
π€ 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.
π 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
π‘Explainable AI
π‘Machine Learning Models
π‘Deep Learning
π‘Black Box
π‘Interpretable Machine Learning
π‘Model Agnostic
π‘Global and Local Approaches
π‘Feature Importance
π‘Surrogate Models
π‘LIME
π‘Counterfactual Explanations
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.
Transcripts
[Music]
hello and welcome to this new series
which is called explainable ai explained
in the next couple of videos i will
explain and talk about how we can better
understand machine learning models
using different methods from the
research field explainable ai
these are things like what is our model
learning which part of the input is most
important for a prediction
or also how do we ensure our model is
robust ai systems and machine learning
algorithms
are widespread in many areas nowadays
data is used almost everywhere to solve
problems and help humans
a large factor for this success is the
progress in the deep learning area
but also generally the development of
new and creative ways how we can use
data
as a consequence the complexity of these
systems becomes
incomprehensible even for ai experts
that's why the models are usually also
referred to as black boxes
however machine learning is also applied
in safety and health critical
environments
such as autonomous driving or the
healthcare sector
therefore humans simply need to
understand what is going on
inside their algorithms
and that's exactly where explainable ai
comes into play
this research field often also called
interpretable machine learning provides
techniques to better understand and
validate how our machine learning models
work
as this google trends graph shows the
interest in this field has increased
over the last couple of years
understanding the models is not only
relevant for data scientists to build
them but especially also the end users
that expect
explanations why certain decisions are
made transparency and interpretability
can therefore be even seen as some sort
of user experience
all of this might sound a bit abstract
now but we will soon see a real example
in the series for which we will come to
appreciate the help of explainable ai
usually when building models from data a
trade-off can be observed
either we have simple linear models that
can be easily interpreted by humans
but might not lead to superb predictions
for complex problems
or we build highly non-linear models
that provide a better performance on
most tasks but are simply too complex
for humans to understand
neural networks for instance often have
millions of parameters which simply
exceeds the human capabilities
therefore we generally have two options
either we ensure that the trained
machine learning algorithm can be
interpreted
or we need to derive human
understandable explanations of a complex
trained model
in the literature this is usually called
model based or
post hoc post hoc methods can be further
divided into black box approaches in
white box approaches
black box approaches means we don't know
anything about the model
we only use the relationship between
inputs and outputs
so the response function to derive
explanations
for white box approaches however we have
access to the model internals
that means for instance we can access
gradients or weights of a neural network
the field explainable ai entails the
whole psychology area about
what makes good explanations and which
explanation types are the best for
humans but we won't really talk about
this in the next videos but rather the
different algorithms that exist
there's a github project called awesome
machine learning interoperability
that includes this nice overview on
different explainable ai methods
further down you also find a lot of
python in our packages
with the corresponding implementations
so before you start coding make sure to
check this out
let's quickly talk about the terminology
used in this research field
the different types of methods can be
distinguished according to a couple of
properties
first of all we can differentiate
between model agnostic and model
specific explainable ai methods
model agnostic means the explainable ai
algorithm can be applied on any kind of
model
so random forest neural network or
support vector machine
model specific on the other hand means
that the method was designed for a
specific type of machine learning model
such as
only for neural networks regarding the
scope of the provided explanations we
can
categorize the methods into global and
local approaches
this refers to either aiming to explain
the whole model
or just parts of the model so for
instance individual predictions
to explain this a little bit further
recall that the decision boundary of a
complex model can be
highly non-linear for instance here we
have a classification problem
and only this complex function on the
left can separate the two classes
sometimes it just doesn't make sense to
provide explanations for the global
model
and instead many approaches zoom into a
local area
then they explain the individual
predictions made at that decision
boundary
we will talk about this in more detail
when we have a look at lime which is a
local explainability approach
besides agnosticity and the scope of a
method we can further differentiate
according to
data type a method can handle not all
explainability algorithms can work with
all data types
finally the models produce different
sorts of explanations
starting with correlation plots or other
visual methods
we can also obtain the information about
the feature importance
this is sometimes also called feature
summary
other methods return even data points
that help us to better understand the
model
finally there exist also approaches that
build a simple model
around the complex one that simple model
can then be used to derive explanations
these models are usually called
surrogates so this is just to give you
an overview on the variety of methods
that have already been developed
for this series i decided to talk about
four explainable ai algorithms
and before that also about the
possibility of working with by design
interpretable machine learning models
a lot of the content comes from the book
interpretable machine learning by
christoph mulner
it's an extensive summary of methods and
extremely well written
so if you're interested in further
details please go and check it out
after talking about interpretable
machine learning models in the first
video
we will have a look at two of the most
popular methods
lime and chap in video four we will have
a closer look at what counterfactual
explanations are
and also quickly talk about adversarial
attacks
the last video introduces slayer-wise
relevance propagation which is a method
specifically designed for neural
networks
these videos are independent of each
other so you can also only watch the
ones you're interested in
one important thing i want to mention is
that there exists also a field called
causal reasoning which is not explicitly
captured in the series
causality is a higher degree of
interpretability
and other methods are used in this field
we will only quickly talk about this in
the counterfactuals video
so that's it for this introduction see
you in the next video where i will also
introduce the practical example for this
series
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