Machines aren’t neutral. They learn from the data we use to train them. But that data is often flawed. 
MIT Technology Review
Weekend Reads
Fighting racial discrimination embedded in tech
9.21.19
Good morning, Weekend Readers!
Machines aren’t neutral. They learn from the data we use to train them. But that data is often flawed. 

It’s becoming clear just how important it is to have broader representation in the design, development, deployment, and governance of technology. 

Read on to see how tech is struggling to deal with its bias problem—and how some researchers are trying to make our world fairer. 

Data mining shows black people waited longer than white people to vote in 2016

The result, obtained from mobile-phone records, raises an obvious question–why?

The connection between video games and mass shootings isn’t just wrong—it’s racist

When a perpetrator is a young white man, people blame video games. If he’s black, people make up a troublingly racist narrative.

Facebook’s ad-serving algorithm discriminates by gender and race

Even if an advertiser is well-intentioned, the algorithm still prefers certain groups of people over others.

Today’s computers can be controlled by the human brain. What’s next?

Hear from experts on the front lines at Future Computes 2019. Register today.

This is how AI bias really happens—and why it’s so hard to fix

Bias can creep in at many stages of the deep-learning process, and the standard practices in computer science aren’t designed to detect it.

AI’s white guy problem isn’t going away

A new report says current initiatives to fix the field’s diversity crisis are too narrow and shallow to be effective.

Google’s algorithm for detecting hate speech is racially biased

AI systems meant to spot abusive online content are far more likely to label tweets “offensive” if they were posted by people who identify as African-American.

But there's hope:

Rediet Abebe uses algorithms and AI to fight socioeconomic inequality

She saw that resources weren’t reaching the people who needed them.

MIT Technology Review
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