Podcast
Questions and Answers
What is primarily responsible for biased decisions in AI according to the data scientist?
What is primarily responsible for biased decisions in AI according to the data scientist?
What is one of the three focuses necessary to improve AI according to the article?
What is one of the three focuses necessary to improve AI according to the article?
What major flaw was observed in the Duke University AI model PULSE?
What major flaw was observed in the Duke University AI model PULSE?
What main effect does undercounting minorities in the 2020 US Census have?
What main effect does undercounting minorities in the 2020 US Census have?
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How could AI potentially impact job opportunities and access to loans?
How could AI potentially impact job opportunities and access to loans?
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Why is there a need for urgent reset in how we handle AI according to the discussion?
Why is there a need for urgent reset in how we handle AI according to the discussion?
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Which of the following is NOT a reason for the potential undercounting of minorities in the impending census?
Which of the following is NOT a reason for the potential undercounting of minorities in the impending census?
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What societal issue does AI's reinforcement of bias primarily affect, as discussed?
What societal issue does AI's reinforcement of bias primarily affect, as discussed?
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What was the estimated number of people omitted in the final counts of the 2010 census?
What was the estimated number of people omitted in the final counts of the 2010 census?
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Which demographic was notably undercounted in the 2010 census?
Which demographic was notably undercounted in the 2010 census?
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What percentage of Aboriginals and Torres Strait populations did the 2016 Australian Census undercount?
What percentage of Aboriginals and Torres Strait populations did the 2016 Australian Census undercount?
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Why is census data considered crucial for AI models that support public services?
Why is census data considered crucial for AI models that support public services?
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What is a primary goal in improving census data quality?
What is a primary goal in improving census data quality?
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What is often ignored in the pursuit of convenience in data collection?
What is often ignored in the pursuit of convenience in data collection?
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Why is collecting data from hard-to-reach rural stores significant?
Why is collecting data from hard-to-reach rural stores significant?
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What is the consequence of urban bias in AI models according to the content?
What is the consequence of urban bias in AI models according to the content?
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What aspect of households is emphasized for data collection in Nielsen panels?
What aspect of households is emphasized for data collection in Nielsen panels?
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Why is the data from households using over-the-air TV reception significant?
Why is the data from households using over-the-air TV reception significant?
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What is the primary mission regarding AI data according to the speaker?
What is the primary mission regarding AI data according to the speaker?
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What is one major implication of an undercount of minorities in the census?
What is one major implication of an undercount of minorities in the census?
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What is described as a common characteristic of minorities regarding participation in censuses?
What is described as a common characteristic of minorities regarding participation in censuses?
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Study Notes
AI and biased data
- AI has the potential to add trillions to the global economy but has not lived up to its promise in fair and equitable policy decision-making
- AI is becoming a gatekeeper to the economy, deciding who gets a job and who gets access to a loan
- AI is reinforcing and accelerating bias at speed and scale with societal implication
Biased Data affects AI decision-making
- The problem is not the algorithm, but the data, particularly biased data
- We need to focus on the data itself to make AI possible for humanity and society
- We need a data reset: focus on data infrastructure, data quality, and data literacy
Examples of Biased Data and its impact
- The Duke University AI model PULSE incorrectly enhanced a nonwhite image into a Caucasian image.
- Underrepresentation in the training set resulted in wrong decisions and predictions.
- The 2020 US Census is a foundation for many social and economic policy decisions and minorities are at risk of being undercounted.
- The 2010 Census undercounted 16 million people in the final count, a number equivalent to the total population of Arizona, Arkansas, Oklahoma, and Iowa combined.
- Undercounting minorities is a common issue in other national censuses, as minorities can be harder to reach, are mistrustful of the government, or live in areas under political unrest.
- The Australian Census in 2016 undercounted Aboriginal and Torres Strait populations by about 17.5 percent.
- Undercounting minorities in the 2020 US Census is expected to be much higher than in 2010 and has massive implications.
Impact on Models and Society
- The Census is the most trusted, open, and publicly available source of rich data on population composition and characteristics, serving as the foundation of our population data infrastructure.
- Undercounting minorities in the Census can lead to AI models supporting Public transportation, housing, healthcare, and insurance overlooking communities in need.
- We need to ensure that databases are representative of age, gender, ethnicity, and race per Census data.
- Investing in data quality and accuracy is essential to making AI accessible to everyone.
Data quality is critical for AI
- Most AI systems use data that's already available or collected for other purposes, which is convenient and cheap, however, data quality is a discipline that requires commitment.
- The definition, data collection, and measurement of bias are often underappreciated and ignored.
- 40% of Chinese and 65% of Indians live in rural areas. The exclusion of this data leads to biased decisions that favor urban over rural populations.
- Without data that represents rural populations, companies will make the wrong investments in pricing, advertising, and marketing. This can also result in wrong rural policy decisions regarding health and other investments.
- Data from rural areas matters and must be included for AI to be fair and effective.
The Importance of Inclusive Data Collection
- Nielsen data science team conducted field visits to collect data from rural stores in China and India, ensuring that data from hard-to-reach locations is included.
- Over-the-air TV viewers constitute 15 percent of US households, a significant group that's very important to marketers, brands, and media companies.
- This group, predominantly Hispanic and African American homes, was included in Nielsen data collection because it is a significant source of ad revenue for broadcasters, including Telemundo and Univision, which deliver free, foundational content for our democracy.
- Inclusive data collection is essential for businesses and society.
Conclusion
- Our opportunity to reduce human bias in AI starts with the data.
- Instead of racing to build new algorithms, we should focus on building a better data infrastructure that makes ethical AI possible.
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Description
This quiz explores the critical topic of biased data in artificial intelligence and its implications for society. Learn about how AI can reinforce societal biases through flawed data and the need for a data reset to ensure equitable outcomes. Delve into real-world examples and the importance of improving data quality and literacy.
