Interview: Ruha Benjamin, author, Race After Technology

Are software algorithms racist? “The idea of a racist robot is a tongue-in-cheek name for machine bias – but I think about the larger process of discriminatory design,” says Ruha Benjamin, an associate professor of African American studies at Princeton University and author of Race After Technology.

Benjamin has a stark warning for the technology industry, software developers and users of technology: technology has the potential to hide, speed up and even deepen discrimination, while appearing neutral and even benevolent when compared to the racist practices of a previous era.

Publicly, the tech industry appears to hold liberal values. Tim Berners-Lee invented the World Wide Web, giving everyone the ability to share information freely. Facebook and other social media platforms have enabled people to connect and share experiences, while Open Source has demonstrated the altruistic nature of freely available software and the fact that programmers dedicate time and effort maintaining open source code.

But Benjamin argues that many algorithms in software systems and online services have discriminatory designs that encode inequality by explicitly amplifying racial hierarchies, by ignoring but thereby replicating social divisions, or by aiming to fix racial bias but ultimately doing quite the opposite.

The risk to society is that these hidden algorithmic biases can therefore have a detrimental effect on minorities. “I want people to think about how automation allows the propagation of traditional biases – even if the machine seems neutral,” she says.

In Race After Technology, Benjamin explores algorithmic discrimination and how existing racial biases get inside data science. “There are many different routes,” she says. “Technology does not grow on trees. What kind of seeds are we planting?”

She asks readers to think about how they can design algorithms differently,  so they are not predisposed to prejudice.

 

 

Ruha Benjamin, Princeton University“I want people to think about how automation allows the propagation of traditional biases”

Because this bias often finds its way into the public sector systems that are responsible for supporting vulnerable members of society, they can amplify racial inequality, she says. For Benjamin, society need to be acutely aware of the societal risk of bias in public sector systems and the systems in finance, insurance, healthcare and other sectors, where officials rely on computer systems to make decisions that can have an adverse impact on individuals.

“It may seem like a very objective system that relies on training data, but if historic data bears strong bias, we are automating these past biases,” she says.

Benjamin would prefer public officials, and those in the private sector responsible for making decisions about individuals, to pull back from their computer screens. “I am trying to resuscitate the human agents behind these systems,” she says.

While people generally recognise their own human bias, for Benjamin, outsourcing decisions to objective systems that have biased algorithms simply shifts that bias to the machine.