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DI DANG: The "People + AI Guidebook"
provides best practices, examples, and tools
to help you create AI-driven products that focus
first and foremost on people.
Developed by Google's People + AI Research team, also known
as PAIR, the guidebook is based on data and insights
from real Google products, teams, and academic research.
Let's walk through the concepts covered in the "People + AI
Guidebook," and touch on how you can
start applying it to your work.
The guidebook has six chapters--
"User Needs and Defining Success,"
"Date Collection and Evaluation," "Mental Models,"
"Explainability and Trust," "Feedback and Controls,"
and "Errors and Graceful Failure."
Each chapter covers unique considerations
that make building AI-driven products
different from designing with other technologies.
To help illustrate these human-centered AI concepts,
we've created an example app that's
used throughout the guidebook.
And to help your team turn this guidance into action,
you can use these exercises in worksheets.
The first chapter, "User Needs and Defining Success,"
can help you answer the question "should we AI this?"
After all, certain problems may be well suited
to being solved with AI, while others may not benefit from it.
We also touch on when it makes sense for an AI
to automate a task entirely or whether it makes more sense
for AI to augment a person's ability
to do that task themselves.
Thirdly, you'll learn how to deliberately design
the reward function, which is how an AI defines successes
and failures.
This has a significant impact on your user's experience.
"Data Collection and Evaluation."
Because training data defines how the AI model works,
the training data you source or collect directly
determines the output of your system and of UX quality.
This chapter helps you understand
what data you need to collect to train a model that
can solve your user's problem.
And last but not least, the chapter
covers tuning, which you do when your model output is not
on target with product goals and user needs.
It's important for us to understand
people's mental models for AI-driven products
so that we can design the AI system to evolve over
time in tandem with their expectations.
Onboarding is crucial for communicating
what an AI-driven product can do,
what it can't, how it can change,
and how the user can make it better.
We also need to plan for co-learning
so that as AI-driven systems learn and adapt,
people's mental models of these systems
will change in response.
"Explainability and Trust."
The right level of explanation into how an AI system works
is key to building the right level of trust with the user.
First, calibrating trust.
AI model output isn't entirely predictable,
so our design decisions need to help people understand when
to trust the system's output and when
to apply their own judgment.
Similarly, it might be critical for the user
to understand how confident the AI
system is in its predictions.
We need to design for if, when, and how the system displays
confidence levels.
The "Feedback and Controls" chapter
looks at AI design considerations
for capturing user feedback in order
to provide the user with a greater
feeling of control and trust.
When we align feedback with model improvement,
we let people know what information is being collected,
what it's for, and how it benefits their experience.
The reality is AI will make an error or fail,
so this chapter looks at how we gracefully
design for these cases.
We also take a look at how you can identify sources of error,
since in an AI system, errors can
appear to the user and system creators in non-intuitive ways.
Last but not least, we discuss designing paths forward
from failure, which can encourage patience
with the system, keep the people-AI relationship going,
and support a better overall experience.
So that's the "People + AI Guidebook,"
offering design and product guidance
for building human-centered AI.
As the field of AI moves forward,
so should the way we design our products.
The guidebook will grow and change, hopefully
with your help.
We'd love to hear your feedback here.
Check out the guidebook-- link in the description below--
and subscribe to our channel for more videos.
Thanks for watching.
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