JEN GENNAI: I'm an operations manager,

so my role is to ensure that we're making our considerations

around ethically AI deliberate, actionable,

and scalable across the whole organization in Google.

So one of the first things to think about

if you're a business leader or a developer

is ensuring that people understand what you stand for.

What does ethics mean to you?

For us, that meant setting values-driven principles

as a company.

These value-driven principles, for us,

are known as our AI principles.

And last year, we announced them in June.

So these are seven guidelines around AI development

and deployment, which assigned to us how

we want to develop AI.

We want to ensure that we're not creating or reinforcing bias.

We want to make sure that we're building technology

that's accountable to people.

And we have five others here that you can read.

It's available on our website.

But at the same time that we announce

these aspirational principles for the company,

we also identified four areas that we

have considered our red lines.

So these are technologies that we will not pursue.

These cover things like weapons technology.

We will not build or deploy weapons.

We will also not build or deploy technologies

that we feel violate international human rights.

So if you're a business leader or a developer,

we'd also encourage you to understand what

are your aspirational goals.

But at the same time, what are your guardrails?

What point are you're not going to cross?

It's the most important thing to do, is to know what is

your definition of ethical AI development.

After you've set your AI principles,

the next thing is, how do you make them real?

How do you make sure that you're aligning with those principles?

So here, there are three main things

I'd suggest keeping in mind.

The first one is you need an accountable and authoritative

body.

So for us in Google, this means that we have senior executives

across the whole company who have the authority

to approve or decline a launch.

So they have to wrestle with some

of these very complex ethical questions

to ensure that we are launching things

that we do believe will lead to fair and ethical outcomes.

So they provide the authority and the accountability

to make some really tough decisions.

Secondly, you have to make sure that the decision-makers have

the right information.

This involves talking to diverse people within the company,

but also listening to your external users,

external stakeholders, and feeding that

into your decision-making criteria.

Jamila will talk more about engaging

with external communites in a moment.

And then the third key part of building governance

and accountability is having operations.

Who's going to do the work?

What are the structures and frameworks

that are repeatable, that are transparent,

and that are understood by the people who

are making these decisions?

So for that, in Google, we've established a central team

that's not based in our engineering and product teams

to ensure that there's a level of objectivity here.

So the same people who are building the products

are not the only people who are looking

to make sure that those products are fair and ethical.

So now you have your principles that you're

trying to ensure that people understand

what does ethics mean for you.

We're talking about establishing governance structure

to make sure that you're achieving those goals,

and the next thing to do is to ensure that you're encouraging

everyone within your company or the people that you work with

and for are aligned on those goals.

So making sure, one, that you've set overall goals in alignment

with ethical AI--

so how are you going to achieve ethical development

and deployment of technology?

Next, you want to make sure that you're training people

to think about these issues from the start.

You don't want to catch some ethical consideration

late in the product development lifecycle.

You want to make sure that you're

starting that as early as possible-- so getting

people trained to think about these types of issues.

Then we have rewards.

You have to make sure if you're holding people

accountable to ethical development and deployment,

you may have to accept that that might slow down

some development in order to get to the right outcomes--

making sure people feel rewarded for thinking

about ethical development and deployment.

And then, finally, making sure that you're hiring people

and developing people who are helping you

achieve those goals.

Next, you've established your frameworks,

you've hired the right people, you're rewarding them.

How do you know you're achieving your goals?

So we think about this as validating and testing.

So an example here is replicating

a user's experience.

Who are your users?

How do you make sure that you're thinking

about a representative sample of your users?

So you think about trying to test different experiences,

mostly from your core subgroups.

But you also want to be thinking about,

who are your marginalized users?

Who might be underrepresented in your workforce?

And therefore, you might have to pay additional attention to

to get it right.

We also think about, what are the failure modes?

And what we mean by that is if people have been negatively

affected by a product in the past,

we want to make sure they won't be negatively affected

in the future.

So how do we learn from that and make sure

that we're testing deliberately for that in the future?

And then the final bit of testing and validation

is introducing some of those failures

into the product to make sure that you're stress testing,

and, again, have some objectivity

to stress test a product to make sure it's achieving

your fair and ethical goals.

And then we think about it's not just you.

You're not alone.

How do we ensure that we're all sharing information

to make us more fair and ethical and to make sure

that the products we deliver are fair and ethical?

So we encourage the sharing of best practices and guidelines.

We do that ourselves in Google by providing

our research and best practices on the Google AI site.

So these best practices cover everything

from ML fairness tools and research

that Margaret Mitchell will talk about in a moment,

but also best practices and guidelines

that any developer or any business leader

could follow themselves.

So we try to both provide that ourselves, as well

as encouraging other people to share their research

and learnings also.

So with that, as we talk about sharing with external,

it's also about bringing voices in.

So I'll pass over to Jamila Smith-Loud

to talk about understanding human impacts.

JAMILA SMITH-LOUD: Thank you.

[APPLAUSE]

Hi, everyone.

I'm going to talk to you a little bit

today about understanding, conceptualizing, and assessing

human consequences and impacts on real people and communities

through the use of tools like social equity impact

assessments.

Social and equity impact assessments

come primarily from the social science discipline

and give us a research-based method

to assess these questions in a way that is broad enough

to be able to apply across products,

but also specific enough for us to think about what

are tangible product changes and interventions that we can make.

So I'll start off with one of the questions

that we often start when thinking about these questions.

I always like to say that when we're

thinking about ethics, when we're thinking about fairness,

and even thinking about questions of bias,

these are really social problems.

And one major entry point into understanding social problems

is really thinking about what's the geographic context in which

users live, and how does that impact their engagement

with the product?

So really asking, what experiences

do people have that are based solely on where they live

and that may differ greatly for other peoples who

live in different neighborhoods that are either

more resourced, more connected to internet-- all

of these different aspects that make regional differences so

important?

Secondly, we like to ask what happens to people when they're

engaging with our products in their families

and in their communities.

We like to think about, what are economic changes that

may come as a part of engagement with this new technology?

What are social and cultural changes that really do impact

how people view the technology and view their participation

in the process?

And so I'll start a little bit of talking about our approach.

The good thing about utilizing kind

of existing frameworks of social and equity impact assessments

which come from--

if you think about when we do new land development

projects or even environmental assessments,

there's already the standard of considering social impacts

as a part of that process.

And so we really do think of employing new technologies

in the same way.

We should be asking similar questions about how communities

are impacted, what are their perceptions,

and how are they framing these engagements?

And so one of the things that we think about

are kind of what is a principled approach to asking

these questions?

And the first one really is around

engaging in the hard questions.

When we're talking about fairness,

when we're talking about ethics, we're

not talking about them separately

from issues of racism, social class, homophobia,

and all forms of cultural prejudice.

We're talking about what are the issues as they

overlay in those systems.?

And so it really requires us to be

OK with those hard questions, and engaging with them,

and realizing that our technologies and our products

don't exist separately from that world.

The next approach is really towards thinking anticipatory.

I think the different thing about thinking

about social and equity impact assessments

from other social science research methods

is that the relationships between causal impacts

and correlations are going to be a little bit different,

and we really are trying to anticipate

harms and consequences.

And so it requires you to be OK with the fuzzy conversations,

but also realize that there's enough research,

there's enough data that gives us

the understanding of how history and contexts impact outcomes.

And so being anticipatory in your process

is really, really an important part of it.

And lastly, in terms of thinking about the principled approach

is really centering the voices and experiences

of those communities who often bear the burden

of the negative impacts.

And that requires understanding how

those communities would even conceptualize these problems.

I think sometimes we come from a technical standpoint,

and we think about the communities

as separate from the problem.

But if we're ready to center those voices and engaged

throughout the whole process, I think

it results in better outcomes.

So to go a little bit deeper into engaging

in the hard questions, what we're really trying to do

is be able to assess how a product will impact

communities, particularly communities

who have been historically and traditionally marginalized.

So it requires us to really think