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An ambitious scientific study conducted in Germany shows how discrimination can work on a spectrum. It also shows how anti-Muslim bigotry is affected by how much the target appears to have assimilated into mainstream society.

"It's a common argument, mainly by parties on the right, that immigrants are resistant to integrating," Nicholas Sambanis, a political scientist at the University of Pennsylvania, told the Los Angeles Times.

"They justify conflict and negative attitudes toward immigration and arguments to reduce immigration by referencing these fears that immigrants don't want to integrate," he continued.

To see whether an immigrants perceived adaptation of cultural norms affects the level of discrimination they face, Sambanis and two of his partners conducted a social experiment in 29 train stations that involved over 7,000 bystanders who unwillingly became test subjects.
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Seattle’s Police Union President Has A Message All Cops Should Hear

There’s no room for biases in this police union. Don’t like it? Leave.

image via (cc) flickr user clappstar

When it comes to questions of race and bias in law enforcement, many were surprised to hear FBI Director James Comey speak so candidly on the subject while presenting at Georgetown University this past week. There, Comey reportedly talked about the need for police officers to "understand the people we serve and protect — by trying to know, deep in our gut, what it feels like to be a law-abiding young black man walking on the street and encountering law enforcement.” It’s a speech that can been seen in part as a response to the growing chorus of criticism leveled at law enforcement following the deaths of unarmed black men like Eric Garner, Tamir Rice, and Mike Brown, and for many concerned about the frequently fraught relationship between police and minority communities, it’s a welcome step in a positive direction. But while Comey’s speech may have been the national headline-grabber, this past week saw another law enforcement leader speaking just as frankly–if not more so–about those same concerns.

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Micro-Bias: If You Want a Kiva Loan, It Helps to Be Pretty and Light-Skinned

Micro-lending sites like Kiva are an important development tool, but bias is affecting lending choices. Here's how to fix it.

Want to cash in on the surge in online peer-to-peer lending? As with many things in life, it helps to have a pretty smile. Sadly, it also helps to have lighter skin.

People who lend on tend to favor attractive, light-skinned females, according to an analysis set to be released by researchers at Nanyang Technological University in Singapore. To receive a loan online, it’s better to be pretty than ugly, female than male, skinny than fat and, yes, light-skinned than dark-skinned, the researchers found. “So, basically if you have any of these characteristics, you will get your money more quickly on,” says Walter Theseira, the lead author of the study.

Kiva allows internet users to browse candidates for charitable microloans around the world, sort by country or type of project, and then—while viewing a picture of the potential borrower—click a few buttons to lend to a microfinance agency supporting that individual. Kiva recently launched a pilot that allows users to lend directly to borrowers without the middleman, but the study focused exclusively on the main Kiva site.

Theseira conducted the study with Christina Jenq at the University of Chicago’s Booth School of Business and Jessica Pan at the National University of Singapore. The researchers used a skin color scale used in research on bias against new immigrants and found conclusively that lighter-skinned borrowers got loans faster. Being attractive was even more helpful.

“This is why [NGOs] spend so much time choosing just the right photographs to illicit donations,” Theseira says. People respond more, and with donations, to pretty, smiling faces. But “as for why people do that... it’s a bit hard to say,” he cautions. “Our hypothesis is this is probably more a form of implicit discrimination than people acting on explicit bias.”

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