Big Data’s Biases, and the Artists and Designers Filling in the Blank Spots  
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This week we explore data bias and exclusion, and speak with designers, developers, and artists  
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about why missing data so often intersects with the interests of the most vulnerable.  
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Finding the Blank Spots in Big Data  
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finding the blank spots in big…  
How often do we think of data as missing?  
Data is everywhere it’s used to decide what products to  
stock in stores, to determine which diseases we’re most at risk for, to train AI models to think  
more like humans. It’s collected by our governments and used to make civic decisions.  
It’s mined by  
major tech companies and sold to advertisers.  
As our data becomes an increasingly valuable  
commodity, for your data to be  missing  to not be  seen  or counted can almost seem like a good  
thing.  
On the other hand, when data is used at such an enormous scale, gaps take on an outsized  
importance.  
In her new book, Invisible Women: Exposing Data Bias in a World Designed for Men,  
Caroline Criado Perez argues that the lack of data collected on women not only naturalizes sex and  
gender discrimination, it also quite literally endangers women’s health (e dot g.  
when the crash test  
dummy is only designed for average male proportions).  
Missing data can also lead to erasure,  
reinforce bias, and, ultimately, create a distorted view of humanity.  
As Tea Uglow, director of  
Google’s Creative Lab, has put it,  If the data does not exist, you do not exist.  
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If the data does not exist, you do not exist.  
Today, there’s an increasing number of artists, programmers, designers, and technologists that are  
addressing this problem at a grassroots level.  
These creatives understand that those most at risk  
for being excluded from data are also the most marginalized, and they’ve decided to use data as a  
way to protest and challenge who has access to power and who doesn’t.  
They also bring idealistic,  
communal, and at times poetic approaches to the biggest hurdle when dealing with  blank  
spots  having to address something that isn’t there.  
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Read more about the individuals who are working to find and fill the blank spots in big data, and  
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what large tech companies and institutions could learn from them.  
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