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  • Transcriber: Leslie Gauthier Reviewer: Joanna Pietrulewicz

  • AI could add 16 trillion dollars to the global economy

  • in the next 10 years.

  • This economy is not going to be built by billions of people

  • or millions of factories,

  • but by computers and algorithms.

  • We have already seen amazing benefits of AI

  • in simplifying tasks,

  • bringing efficiencies

  • and improving our lives.

  • However, when it comes to fair and equitable policy decision-making,

  • AI has not lived up to its promise.

  • AI is becoming a gatekeeper to the economy,

  • deciding who gets a job

  • and who gets an access to a loan.

  • AI is only reinforcing and accelerating our bias

  • at speed and scale

  • with societal implications.

  • So, is AI failing us?

  • Are we designing these algorithms to deliver biased and wrong decisions?

  • As a data scientist, I'm here to tell you,

  • it's not the algorithm,

  • but the biased data

  • that's responsible for these decisions.

  • To make AI possible for humanity and society,

  • we need an urgent reset.

  • Instead of algorithms,

  • we need to focus on the data.

  • We're spending time and money to scale AI

  • at the expense of designing and collecting high-quality and contextual data.

  • We need to stop the data, or the biased data that we already have,

  • and focus on three things:

  • data infrastructure,

  • data quality

  • and data literacy.

  • In June of this year,

  • we saw embarrassing bias in the Duke University AI model

  • called PULSE,

  • which enhanced a blurry image

  • into a recognizable photograph of a person.

  • This algorithm incorrectly enhanced a nonwhite image into a Caucasian image.

  • African-American images were underrepresented in the training set,

  • leading to wrong decisions and predictions.

  • Probably this is not the first time

  • you have seen an AI misidentify a Black person's image.

  • Despite an improved AI methodology,

  • the underrepresentation of racial and ethnic populations

  • still left us with biased results.

  • This research is academic,

  • however, not all data biases are academic.

  • Biases have real consequences.

  • Take the 2020 US Census.

  • The census is the foundation

  • for many social and economic policy decisions,

  • therefore the census is required to count 100 percent of the population

  • in the United States.

  • However, with the pandemic

  • and the politics of the citizenship question,

  • undercounting of minorities is a real possibility.

  • I expect significant undercounting of minority groups

  • who are hard to locate, contact, persuade and interview for the census.

  • Undercounting will introduce bias

  • and erode the quality of our data infrastructure.

  • Let's look at undercounts in the 2010 census.

  • 16 million people were omitted in the final counts.

  • This is as large as the total population

  • of Arizona, Arkansas, Oklahoma and Iowa put together for that year.

  • We have also seen about a million kids under the age of five undercounted

  • in the 2010 Census.

  • Now, undercounting of minorities

  • is common in other national censuses,

  • as minorities can be harder to reach,

  • they're mistrustful towards the government

  • or they live in an area under political unrest.

  • For example,

  • the Australian Census in 2016

  • undercounted Aboriginals and Torres Strait populations

  • by about 17.5 percent.

  • We estimate undercounting in 2020

  • to be much higher than 2010,

  • and the implications of this bias can be massive.

  • Let's look at the implications of the census data.

  • Census is the most trusted, open and publicly available rich data

  • on population composition and characteristics.

  • While businesses have proprietary information

  • on consumers,

  • the Census Bureau reports definitive, public counts

  • on age, gender, ethnicity,

  • race, employment, family status,

  • as well as geographic distribution,

  • which are the foundation of the population data infrastructure.

  • When minorities are undercounted,

  • AI models supporting public transportation,

  • housing, health care,

  • insurance

  • are likely to overlook the communities that require these services the most.

  • First step to improving results

  • is to make that database representative

  • of age, gender, ethnicity and race

  • per census data.

  • Since census is so important,

  • we have to make every effort to count 100 percent.

  • Investing in this data quality and accuracy

  • is essential to making AI possible,

  • not for only few and privileged,

  • but for everyone in the society.

  • Most AI systems use the data that's already available

  • or collected for some other purposes

  • because it's convenient and cheap.

  • Yet data quality is a discipline that requires commitment --

  • real commitment.

  • This attention to the definition,

  • data collection and measurement of the bias,

  • is not only underappreciated --

  • in the world of speed, scale and convenience,

  • it's often ignored.

  • As part of Nielsen data science team,

  • I went to field visits to collect data,

  • visiting retail stores outside Shanghai and Bangalore.

  • The goal of that visit was to measure retail sales from those stores.

  • We drove miles outside the city,

  • found these small stores --

  • informal, hard to reach.

  • And you may be wondering --

  • why are we interested in these specific stores?

  • We could have selected a store in the city

  • where the electronic data could be easily integrated into a data pipeline --

  • cheap, convenient and easy.

  • Why are we so obsessed with the quality

  • and accuracy of the data from these stores?

  • The answer is simple:

  • because the data from these rural stores matter.

  • According to the International Labour Organization,

  • 40 percent Chinese

  • and 65 percent of Indians live in rural areas.

  • Imagine the bias in decision

  • when 65 percent of consumption in India is excluded in models,

  • meaning the decision will favor the urban over the rural.

  • Without this rural-urban context

  • and signals on livelihood, lifestyle, economy and values,

  • retail brands will make wrong investments on pricing, advertising and marketing.

  • Or the urban bias will lead to wrong rural policy decisions

  • with regards to health and other investments.

  • Wrong decisions are not the problem with the AI algorithm.

  • It's a problem of the data

  • that excludes areas intended to be measured in the first place.

  • The data in the context is a priority,

  • not the algorithms.

  • Let's look at another example.

  • I visited these remote, trailer park homes in Oregon state

  • and New York City apartments

  • to invite these homes to participate in Nielsen panels.

  • Panels are statistically representative samples of homes

  • that we invite to participate in the measurement

  • over a period of time.

  • Our mission to include everybody in the measurement

  • led us to collect data from these Hispanic and African homes

  • who use over-the-air TV reception to an antenna.

  • Per Nielsen data,

  • these homes constitute 15 percent of US households,

  • which is about 45 million people.

  • Commitment and focus on quality means we made every effort

  • to collect information

  • from these 15 percent, hard-to-reach groups.

  • Why does it matter?

  • This is a sizeable group

  • that's very, very important to the marketers, brands,

  • as well as the media companies.

  • Without the data,

  • the marketers and brands and their models

  • would not be able to reach these folks,

  • as well as show ads to these very, very important minority populations.

  • And without the ad revenue,

  • the broadcasters such as Telemundo or Univision,

  • would not be able to deliver free content,

  • including news media,

  • which is so foundational to our democracy.

  • This data is essential for businesses and society.

  • Our once-in-a-lifetime opportunity to reduce human bias in AI

  • starts with the data.

  • Instead of racing to build new algorithms,

  • my mission is to build a better data infrastructure

  • that makes ethical AI possible.

  • I hope you will join me in my mission as well.

  • Thank you.

Transcriber: Leslie Gauthier Reviewer: Joanna Pietrulewicz

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