For example, Melanie Dawes, CEO of Ofcom, which regulates social media in the UK, has said that social media platforms will have to explain how their code works. And the recently passed European Union Digital Services Act, agreed on April 23, will also force platforms to provide transparency about algorithms. In the United States, Democratic senators tabled proposals for an Algorithmic Accountability Act in February 2022. Their goal is to provide new transparency and oversight of the algorithms that govern our timelines and news, and more.
Allowing Twitter’s algorithm to be visible to others and adaptable to competitors theoretically means that someone could just copy the Twitter source code and launch their own rebranded version. Much of the Internet is powered by open source software, the most famous being OpenSSL, a set of security tools used by large parts of the global network, which in 2014 suffered a major security breach.
There are even examples of open source social networks. Mastodon, a microblogging platform that was created after concerns about Twitter’s dominant position, is open source, allowing users to inspect code posted to the GitHub software repository.
But looking at the code behind an algorithm doesn’t necessarily tell you how it works, and it certainly doesn’t give the average person a big insight into the business structures and processes involved in creating it.
“It’s a bit like trying to understand ancient creatures with only genetic material,” says Jonathan Gray, a tenured professor of critical infrastructure studies at King’s College London. “It tells us more than anything, but it would be an exaggeration to say we know how they live.”
Nor is there a single algorithm that controls Twitter. “Some of them will determine what people see in their timelines in terms of trends, content, or suggested follow-up,” says Catherine Flick, a researcher in computer science and social responsibility at De Montfort University in Leicester. The algorithms that people will be primarily interested in are those that control what content appears on users’ timelines, but even that won’t be very useful without the training data.
“Most of the time, when people talk about algorithmic responsibility these days, we recognize that the algorithms themselves aren’t necessarily what we want to see; what we really want is information about how they developed,” says Jennifer Cobbe. postdoctoral research associate. at Cambridge University. This is largely due to concerns about AI algorithms that perpetuate human biases in the data used to train them. Who develops algorithms and what data they use can make a significant difference in the results they spit out.
For Cobbe, the risks outweigh the potential benefits. The computer code gives us no idea how the algorithms were trained or tested, what factors or considerations were included, or what kind of things were prioritized in the process.
At the same time, its open source algorithm may not make a significant difference to transparency on Twitter, and may introduce some significant security risks.
Companies often publish their data protection impact assessments, which probe and test systems to highlight weaknesses and defects. When discovered, they are corrected, but the data is often deleted to avoid security risks. Using open source Twitter algorithms, the entire code base of the website would be accessible to everyone, which could allow malicious actors to study the software and find vulnerabilities to exploit.
“I don’t think for a moment that Elon Musk is looking to get open source for all of Twitter’s infrastructure and security,” says Eerke Boiten, a professor of cybersecurity at De Montfort University in Leicester.