Name: 機械学習の公平性。学んだ教訓（Google I/O'19 (Machine Learning Fairness: Lessons Learned (Google I/O'19))
Uploaded: 2021-01-14T10:03:56.000Z
Duration: 36 min 23 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

I'm Jackie, and I'm the Lead Program Manager on ML Fairness

As some of you may know, Google's mission

and make it universally accessible and useful.

Every one of our users gives us their trust.

And it's our responsibility to do right by them.

it's critical to ethically design and deploy

these systems in a fair and inclusive way.

Addressing fairness in AI is an active area

embodies critical and diverse knowledge to training models

There is no standard definition of fairness,

whether decisions are made by humans or by machines.

Far from a solved problem, fairness in AI

presents both an opportunity and a challenge.

to guide the responsible development and use of AI.

One of them directly speaks to ML fairness

and making sure that our technologies don't create

seek to avoid unjust impacts on people related

to sensitive characteristics such as race, ethnicity,

gender, nationality, income, sexual orientation, ability,

Now let's take a look at how unfair bias might be created

acknowledging that humans are at the center of technology

design, in addition to being impacted by it.

And humans have not always made product design decisions

that are in line with the needs of everyone.

For example, because female body-type crash test dummies

weren't required until 2011, female drivers

Band-Aids have long been manufactured in a single

In this tweet, you see the personal experience

of an individual using a Band-Aid that matches his skin

A product that's designed and intended for widespread use

shouldn't fail for an individual because of something

Products and technology should just work for everyone.

These choices may not have been deliberate,

design and the impact it may have on humans.

Why does Google care about these problems?

Well, our users are diverse, and it's important

that we provide an experience that works equally

The good news is that humans, you, have the power

and to create technology that is fair and more

I'll give you a sense of what that means.

You'll notice where the label "wedding" was applied

to the images on the left, and where it wasn't, the image

how one open source image classifier trained on the Open

Images Dataset does not properly recognize wedding traditions

Open datasets, like open images, are a necessary and critical

been found to be geographically skewed based on how

to open images, last year, we enabled the global community

of crowdsourced app users to photograph the world

around them and make their photos available to researchers

and developers as a part of the Open Images Extended Dataset.

We know that this is just an early step on a long journey.

training data must represent global diversity

These are complex sociotechnical challenges,

and they need to be interrogated from many different angles.

you think about these systems with human impact in mind.

Let's talk a little bit more about these challenges

and where they can manifest in an ML pipeline.

Unfairness can enter the system at any point in the ML

pipeline, from data collection and handling to model training

Rarely can you identify a single cause of or a single solution

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機械学習の公平性。学んだ教訓（Google I/O'19 (Machine Learning Fairness: Lessons Learned (Google I/O'19))

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