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  • [MUSIC PLAYING]

  • 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