AI is making inequality worse

In the U.S., for example, during most of the 20th century, various regions of the country ભાષા in the language of economists હતા were “converging” and financial inequalities decreased. Then, in the 1980’s, the onslaught of digital technology came, and the trend itself changed. Automation ruined many manufacturing and retail jobs. New, well-paying tech jobs were clustered in a few cities.

According to the Brookings Institution, the shortlist of eight American cities, including San Francisco, San Jose, Boston and Seattle, accounted for about 38% of all tech jobs by 2019. New AI technologies are particularly focused: Brookings’ Mark Murrow and Sifan Liu estimate that only 15 cities account for two-thirds of the AI ​​assets and capabilities in the United States (San Francisco and San Jose alone account for about a quarter).

The dominance of some cities in the search and commercialization of AI means that geographical disparities in wealth will continue to grow. This could lead not only to political and social unrest, but also to the AI ​​technologies needed for the development of regional economies, as the cuckoo suggests.

Big Tech can be part of the solution to somehow easing the confusion over defining the AI ​​agenda. It will increase federal funding for research independent of potential tech giants. Muro and others have suggested heavy federal funding to help build U.S. regional innovation centers, for example.

A more immediate response is to expand our digital vision to envision AI technology that not only replaces jobs but expands opportunities in areas where different parts of the country care the most, such as healthcare, education and manufacturing.

Mind changing

The hobby that AI and robotics researchers have to simulate human abilities means trying to get a machine to do simple but daunting work for technology. Making a bed, for example, or espresso. Or driving a car. It’s wonderful to see an autonomous car navigating the city streets or to have a robot work as a barista. But often, those who develop and use this technology do not think much about the potential impact on jobs and labor markets.

Anton Corinick, an economist at the University of Virginia and Rubenstein Fellow of Brookings, says the billions of dollars spent on building autonomous cars will inevitably have a negative impact on labor markets once such vehicles are deployed, which will take jobs. Numerous drivers. If, he asks, those billions have been invested in AI tools that would be more likely to expand labor opportunities?

Applying for funding at places like the US National Science Foundation and the National Institutes of Health, Corinne explains, “No one asks ‘how will it affect the labor markets?'”

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Katya Klinova, a partnership policy specialist on AI in San Francisco, is working on ways for AI scientists to rethink the way they measure success. “When you look at AI research, and you see the universally used benchmarks, it’s all about matching or comparing human performance,” she says. That is, AI scientists grade their applications into image recognition, according to how well a person can recognize an object.

Klinova says such criteria have moved in the direction of research. “It’s no surprise that it turned out to be automation and more powerful automation,” she adds. “The benchmark is very important for AI developers – especially for young scientists who are entering AI in large numbers and asking ‘What should I do?'”

But the criteria for demonstrating human-machine collaboration are lacking, Klinova says, although she has begun working to help create some. In collaboration with Corinne, she and her team in partnership for AI are also writing a user guide for AI developers who have no background in economics to help them understand how research could influence workers.

“It’s about changing a far-fetched narrative where AI researchers are given empty tickets to disrupt and then it’s up to society and government to deal with it,” says Klinova. Every AI firm has some sort of answer about AI bias and ethics, she says, “but they’re still not there for labor effects.”

The epidemic has accelerated the digital transition. Businesses have turned to understandable automation to replace workers. But the epidemic has also drawn attention to the potential of digital technologies to expand our capabilities. They have given us research tools to help us make new vaccines and have provided the right way for many to work from home.

As AI inevitably expands its impact, it will be worthwhile to see if this does more harm to good jobs – and more inequality. “I’m hopeful we’ll be able to run the technology properly,” says Brynjolfsson. But, he adds, it would mean making a deliberate choice about the technology we create and invest in.


“The Turing Trap: The Promise and Risk of Artificial Intelligence as a Human”
Eric Brynjolfsson
Daedalus, Spring 2022

“The Wrong Type of AI? The Future of Artificial Intelligence and Labor Demand”
Deron Asemoglu and Pasquel Restrepo
Cambridge Journal of Region, Economy and Society, March 2020

Cogs and Monsters: What Economics Is and What It Should Be
Diane Cuckoo
Princeton University Press

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