Jun 28, 2022 07:06 PM
(This post was last modified: Jun 28, 2022 10:37 PM by C C.)
Rich people who grew up poor are less likely to be sympathetic to the poor than those born wealthy
https://www.science20.com/news_staff/ric...thy-256131
EXCERPTS: . . . studies showed that young people who espouse more liberal beliefs get more conservative when they are inebriated. They stop saying what they think they should be saying based on what people want to hear. Along that line, a wealthy person who was raised poor is more likely to see through excuses of poor people than someone born into money, according to a new paper. They are less 'sympathetic' than people who have never had to struggle.
[...] people viewed those who became rich (the Became Rich) more positively than those who were born rich (the Born Rich) ... So they surveyed 1,032 relatively wealthy individuals in the U.S. (with annual incomes over $80,000 in one study and over $142,501 in another) and found that those who became rich thought it was easier to improve one’s socioeconomic status than people who were born rich, and this, in turn, predicted reduced sympathetic attitudes toward the poor.
Rich people know they were born rich. They got lucky. This may be why foundations created by wealthy people get more progressive over time. The founders did the work, and know how to go from nothing to something, but their children may see their parents as part of the problem in capitalism... (MORE - missing details)
- - - - - -
IOW, the pertinent Born Rich probably experience guilt, and perhaps contingently some degree of self-despising. They need "feel-good" fixes accommodated by that platitudinal road of good intentions, that eventually spirals off into Marxist offshoots Hell. It's paved by elites trying to expeditiously unload or salve their guilt, with minimum critical thought applied to consequences.
People less outraged by gender discrimination caused by algorithms
https://www.fs.usda.gov/pnw/news-release...-wildfires
RELEASE: People are less morally outraged when gender discrimination occurs because of an algorithm rather than direct human involvement, according to research published by the American Psychological Association.
In the study, researchers coined the phrase "algorithmic outrage deficit" to describe their findings from eight experiments conducted with a total of more than 3,900 participants from the United States, Canada and Norway.
When presented with various scenarios about gender discrimination in hiring decisions caused by algorithms and humans, participants were less morally outraged about those caused by algorithms. Participants also believed companies were less legally liable for discrimination when it was due to an algorithm.
"It's concerning that companies could use algorithms to shield themselves from blame and public scrutiny over discriminatory practices," said lead researcher Yochanan Bigman, PhD, a post-doctoral research fellow at Yale University and incoming assistant professor at Hebrew University. The findings could have broader implications and affect efforts to combat discrimination, Bigman said. The research was published online in the Journal of Experimental Psychology: General.
"People see humans who discriminate as motivated by prejudice, such as racism or sexism, but they see algorithms that discriminate as motivated by data, so they are less morally outraged," Bigman said. "Moral outrage is an important societal mechanism to motivate people to address injustices. If people are less morally outraged about discrimination, then they might be less motivated to do something about it."
Some of the experiments used a scenario based on a real-life example of alleged algorithm-based gender discrimination by Amazon that penalized female job applicants. While the research focused on gender discrimination, one of the eight experiments was replicated to examine racial and age discrimination and had similar findings.
Knowledge about artificial intelligence didn't appear to make a difference. In one experiment with more than 150 tech workers in Norway, participants who reported greater knowledge about artificial intelligence were still less outraged by gender discrimination caused by algorithms.
When people learn more about a specific algorithm it may affect their outlook, the researchers found. In another study, participants were more outraged when a hiring algorithm that caused gender discrimination was created by male programmers at a company known for sexist practices.
Programmers should be aware of the possibility of unintended discrimination when designing new algorithms, Bigman said. Public education campaigns also could stress that discrimination caused by algorithms may be a result of existing inequities, he said.
https://www.science20.com/news_staff/ric...thy-256131
EXCERPTS: . . . studies showed that young people who espouse more liberal beliefs get more conservative when they are inebriated. They stop saying what they think they should be saying based on what people want to hear. Along that line, a wealthy person who was raised poor is more likely to see through excuses of poor people than someone born into money, according to a new paper. They are less 'sympathetic' than people who have never had to struggle.
[...] people viewed those who became rich (the Became Rich) more positively than those who were born rich (the Born Rich) ... So they surveyed 1,032 relatively wealthy individuals in the U.S. (with annual incomes over $80,000 in one study and over $142,501 in another) and found that those who became rich thought it was easier to improve one’s socioeconomic status than people who were born rich, and this, in turn, predicted reduced sympathetic attitudes toward the poor.
Rich people know they were born rich. They got lucky. This may be why foundations created by wealthy people get more progressive over time. The founders did the work, and know how to go from nothing to something, but their children may see their parents as part of the problem in capitalism... (MORE - missing details)
- - - - - -
IOW, the pertinent Born Rich probably experience guilt, and perhaps contingently some degree of self-despising. They need "feel-good" fixes accommodated by that platitudinal road of good intentions, that eventually spirals off into Marxist offshoots Hell. It's paved by elites trying to expeditiously unload or salve their guilt, with minimum critical thought applied to consequences.
People less outraged by gender discrimination caused by algorithms
https://www.fs.usda.gov/pnw/news-release...-wildfires
RELEASE: People are less morally outraged when gender discrimination occurs because of an algorithm rather than direct human involvement, according to research published by the American Psychological Association.
In the study, researchers coined the phrase "algorithmic outrage deficit" to describe their findings from eight experiments conducted with a total of more than 3,900 participants from the United States, Canada and Norway.
When presented with various scenarios about gender discrimination in hiring decisions caused by algorithms and humans, participants were less morally outraged about those caused by algorithms. Participants also believed companies were less legally liable for discrimination when it was due to an algorithm.
"It's concerning that companies could use algorithms to shield themselves from blame and public scrutiny over discriminatory practices," said lead researcher Yochanan Bigman, PhD, a post-doctoral research fellow at Yale University and incoming assistant professor at Hebrew University. The findings could have broader implications and affect efforts to combat discrimination, Bigman said. The research was published online in the Journal of Experimental Psychology: General.
"People see humans who discriminate as motivated by prejudice, such as racism or sexism, but they see algorithms that discriminate as motivated by data, so they are less morally outraged," Bigman said. "Moral outrage is an important societal mechanism to motivate people to address injustices. If people are less morally outraged about discrimination, then they might be less motivated to do something about it."
Some of the experiments used a scenario based on a real-life example of alleged algorithm-based gender discrimination by Amazon that penalized female job applicants. While the research focused on gender discrimination, one of the eight experiments was replicated to examine racial and age discrimination and had similar findings.
Knowledge about artificial intelligence didn't appear to make a difference. In one experiment with more than 150 tech workers in Norway, participants who reported greater knowledge about artificial intelligence were still less outraged by gender discrimination caused by algorithms.
When people learn more about a specific algorithm it may affect their outlook, the researchers found. In another study, participants were more outraged when a hiring algorithm that caused gender discrimination was created by male programmers at a company known for sexist practices.
Programmers should be aware of the possibility of unintended discrimination when designing new algorithms, Bigman said. Public education campaigns also could stress that discrimination caused by algorithms may be a result of existing inequities, he said.