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Extraction Summary

2
People
5
Organizations
2
Locations
0
Events
1
Relationships
5
Quotes

Document Information

Type: Congressional oversight record / interview transcript or essay
File Size:
Summary

This page (140) from a House Oversight document (stamped 016360) appears to be a transcript or essay discussing the societal impacts of 'Extreme Wealth' and 'AI and Society.' The speaker defends billionaire philanthropy, specifically citing Bill Gates, the Ford Foundation, and the Sloan Foundation as entities filling gaps left by the government. The text also contrasts US wealth mobility favorably against European hereditary wealth and argues for a data-centric approach to regulating Artificial Intelligence, drawing an analogy between AI algorithms and government bureaucracies.

People (2)

Name Role Context
Bill Gates Philanthropist / Billionaire
Cited as the 'most familiar example' of wealthy individuals pledging to give away wealth and taking action where gove...
Speaker/Author Interviewee or Essayist
Unidentified in this specific page text, but speaks in the first person ('I would worry', 'We need to make oversight....

Organizations (5)

Name Type Context
Ford Foundation
Cited as an organization that bets on things others wouldn't.
Sloan Foundation
Cited alongside Ford Foundation for changing the world for the better.
The Giving Pledge
Referenced in footnote 36 regarding the pledge of wealthy people to give away 50% of wealth.
European Union (E.U.)
Mentioned in relation to setting up a trust-network framework for AI.
House Oversight Committee
Source of the document (indicated by Bates stamp).

Locations (2)

Location Context
Contrasted with Europe regarding hereditary wealth.
Described as having entrenched wealth held by families for hundreds of years.

Relationships (1)

Bill Gates Participant/Example The Giving Pledge
Bill Gates is cited as the most familiar example of those pledging to give away wealth.

Key Quotes (5)

"He’s decided that if the government won’t do it, he’ll do it. You want mosquito nets? He’ll do it. You want antivirals? He’ll do it."
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Quote #1
"Actions from outside government by organizations like the Ford Foundation and the Sloan Foundation, who bet on things that nobody else would bet on, have changed the world for the better."
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Quote #2
"If you win the lottery, you get your billion dollars, but your grandkids ought to work for a living."
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Quote #3
"Without data, AI is nothing. You don’t have to watch the AI; instead you should watch what it eats and what it does."
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Quote #4
"The most revealing analogy is that regulators, bureaucracies, and parts of the government are very much like AIs: They take in the rules that we call law and regulation, and they add government data, and they make decisions that affect our lives."
Source
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Quote #5

Full Extracted Text

Complete text extracted from the document (3,445 characters)

the same city, nominally, but it’s as if it were two completely different cities—and this is perhaps the most important cause of today’s plague of polarization.
On Extreme Wealth
Some two hundred of the world’s wealthiest people have pledged to give away more than 50 percent of their wealth either during their lifetimes or in their wills, creating a plurality of voices in the foundation space.36 Bill Gates is probably the most familiar example. He’s decided that if the government won’t do it, he’ll do it. You want mosquito nets? He’ll do it. You want antivirals? He’ll do it. We’re getting different stakeholders to take action in the form of foundations dedicated to public good, and they have different versions of what they consider the public good. This diversity of goals has created a lot of what’s wonderful about the world today. Actions from outside government by organizations like the Ford Foundation and the Sloan Foundation, who bet on things that nobody else would bet on, have changed the world for the better.
Sure, these billionaires are human, with human foibles, and all is not necessarily as it should be. On the other hand, the same situation obtained when the railways were first built. Some people made huge fortunes. A lot of people went bust. We, the average people, got railways out of it. That’s good. Same thing with electric power; same thing with many new technologies. There’s a churning process that throws somebody up and later casts them or their heirs down. Bubbles of extreme wealth were a feature of the late 1800s and early 1900s when steam engines and railways and electric lights were invented. The fortunes they created were all gone within two or three generations.
If the U.S. were like Europe, I would worry. What you find in Europe is that the same families have held on to wealth for hundreds of years, so they’re entrenched not just in terms of wealth but of the political system and in other ways. But so far, the U.S. has avoided this kind of hereditary class system. Extreme wealth hasn’t stuck, which is good. It shouldn’t stick. If you win the lottery, you get your billion dollars, but your grandkids ought to work for a living.
On AI and Society
People are scared about AI. Perhaps they should be. But they need to realize that AI feeds on data. Without data, AI is nothing. You don’t have to watch the AI; instead you should watch what it eats and what it does. The trust-network framework we’ve set up, with the help of nations in the E.U. and elsewhere, is one where we can have our algorithms, we can have our AI, but we get to see what went in and what went out, so that we can ask, Is this a discriminatory decision? Is this the sort of thing that we want as humans? Or is this something that’s a little weird?
The most revealing analogy is that regulators, bureaucracies, and parts of the government are very much like AIs: They take in the rules that we call law and regulation, and they add government data, and they make decisions that affect our lives. The part that’s bad about the current system is that we have very little oversight of these departments, regulators, and bureaucracies. The only control we have is the vote—the opportunity to elect somebody different. We need to make oversight of bureaucracies a lot more fine-grained. We need to record the data that went into every single decision
36 https://givingpledge.org/About.aspx.
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