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IRINA SIGLER: Hey, everyone.
I'm thrilled to welcome you to the responsible and Explainable
AI session today.
We are going to start with an overview on responsible AI,
then dive into Vertex AI's offerings on Explainable AI,
with a focus on feature attributions
and example-based explanations.
I'm Irina Sigler.
And I'm a product manager at Vertex AI.
AI systems are developing at an extraordinary pace,
enabling computers to see, understand, and interact
with the world in ways that were unimaginable just a decade ago.
People around the world have become everyday AI users.
And soon technology that isn't AI-enabled
will feel like something is missing.
Global spending on AI systems will jump to over $200 billion
in 2025.
As the speed of AI disruption is on track
to surpass the impact the internet had
on the global economy, it has never
been more important to develop AI
and to develop AI responsibly.
Despite this opportunity, the risk
of building an AI system that does not work for everyone
is not hypothetical.
Many organizations lack trust and have ethical issues
with their AI systems.
From two global surveys, 87% of respondents
said that bias in their data produces
discriminatory results.
65% said that they can't explain how their model's decisions
or predictions are made.
These numbers show a huge risk in not learning and investing
in responsible AI practices to understand how
models are impacting people.
The good news is that we have the power
to approach these problems differently.
At the end of the day, there are a lot
of reasons why ethics and responsibility is important.
At its core, it's the right thing to do.
It is also a critical factor that
will make AI successful for the long-term.
Our approach to our AI is rooted in the idea
that people and businesses, with the best of intentions,
can potentially cause unintended harm.
And we need to understand the risks
and build out processes to avoid those outcomes.
We've developed our own AI principles, practices,
governance processes, and tools to ensure
that the AI we build and enable for the world is successful.
Today we are going to focus on tools.
Responsible AI tools are an increasingly effective way
to inspect and understand AI models.
From our perspective, tooling is an aid, not a solution unto
itself.
Today I'm going to talk about a key pillar
of the responsible AI toolkit, Explainable AI.
In a nutshell, XAI helps you build better models
and make better decisions.
Vertex AI offers a variety of explainability products,
including feature attributions, example-based explanations,
and model analysis toolkits, to understand how your models work
and to take action.
Let's start by having a look at Feature Attributions.
It is an explainability method that
shows users how much each input feature contributed
to their model's prediction.
What Feature Attributions look like
depends on the type of data that you're using.
For each of these, Feature Attributions
will look a bit different.
Let's look at tabular data as an example.
For tabular data, Feature Attributions
tell you how much each feature column
contributed to either a specific prediction or the model
overall.
Data scientists can use Feature Attributions to troubleshoot.
In this case, Feature Attributions
helps to detect that the model picked up on pen marks
by the physician.
Data scientists can also use the product
to monitor models in production to detect drift in Feature
Attributions over time.
In a nutshell, the product provides robust explanations.
Given that AI explainability is still a nascent field,
in developing our XAI products, we were very careful to vet
and invest only in those methods with the most robust technical
and theoretical foundations.
The server supports multiple models and data types,
is easy to use and scale, and integrated into a number
of Vertex services and BQML.
Now let's meet our new approach to explain
AI, example-based explanations.
The focus here is on explaining models' results by looking
at the training data.
In a nutshell, example-based explanations
enable users to understand model behavior
by providing approximate nearest neighbor-based explanations.
We can generate example-based explanations
for multiple types of data, image, text, and tabular.
For images, example-based explanations
will show you which other images in the training data
appeared most similar to the new image
that you want to classify.
For example, for this image of a dog,
the model is classifying it as a Husky,
as all similar examples in the training data
were also labeled as Huskies.
This works very similar for tabular and text data.
The focus is on explaining model results
by showing the most similar examples in the training
dataset.
Data scientists can use Feature Attributions to debug
their machine learning models.
Artificial intelligence is so useful,
because it automatically learns patterns from data
that people might not see.
Of course, models will fail if the underlying training
data has problems, like mislabeled or unclear examples.
Data quality is a constant issue for enterprises.
Without special tools, it is difficult to connect model
failures back to root causes, and even harder
to know what the next step is to resolve these problems.
Machine learning models are often
notoriously hard to debug and troubleshoot.
Our new product addresses this problem.
Say you have a classification model that misclassified
bird as a plane.
We use example-based explanations
to retrieve other images in the training
data that appeared most similar to this misclassified bird
image.
Examining those, we identified that both the misclassified
bird image and the similar images where dark silhouettes.
In turn, this signals a potential lack
of images of birds with dark silhouettes in the training
data.
An immediate next action is to gather more data
with images of silhouetted birds in order to improve the model.
Data scientists can also use Feature Attributions
to empower end users to scrutinize and better
understand ML models.
In this scenario, I'm a clinical pathologist
seeking to recommend a diagnosis based on a cell tissue sample.
I want to closely review the tissue sample
and see similar, relevant samples
from past patients whose diagnosis and outcomes
were known.
It takes only three steps to set up example-based explanations.
First, you upload your model and dataset.
And the service represents the entire dataset
in the latent space.
This space encodes meaningful semantic information,
in this illustrative example, the relationship
between a country and its capital.
Now, after the service indexed your entire dataset,
as a second step, you deploy your index and model,
and the example-based API is ready to query.
You can now query for similar data points,
and only need to repeat steps one and two when
you retrain the model or change your dataset.
Under the hood, the example-based explanations API
builds on cutting-edge technology developed
by Google research that is used at scale across a wide range
of applications, such as Search, YouTube, and Play Store.
In a nutshell, example-based explanations
provides actionable insights to debug your ML models.
The service helps you improve your models by only changing
the underlying training data.
It generates explanations that don't require a prior ML
background, and allow a non-technical audience
to understand and scrutinize your models.
The service is fully managed, and serverless,
and allows querying for similar examples significantly faster
than open source alternatives.
I'll touch upon a few noteworthy examples of customer use cases.
Going from left to right, in YouTube and Ads,
we use XAI for model monitoring.
Ads also uses XAI for feature selection
and to test the importance of new features
they're thinking of adding to the model.
Moving to the third box, a global e-commerce platform
uses XAI as part of their fraud detection system.
They use it for both model debugging as well as
to provide fraud investigators with information
that will help them run more efficient investigations.
Finally, a global semiconductor manufacturer
uses XAI to debug a model they built with AutoML Vision
specifically to uncover a fundamental data formatting
issue.
Thanks for your time.
As a next step, you can try out our services today.
Please feel free to reach out if you have any questions.
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