Unless you’ve been living under a rock, you’ll know the world already has a fake news problem. In the past, at least fake news had to be written by a real person to make it convincing.

OpenAI created an AI which could automatically generate fake stories. Combine fake news, with Cambridge Analytica-like targeting, and the general viral nature of social networks, and it’s easy to understand why OpenAI decided not to make their work public.

On Thursday, two recent master’s degree graduates decided to release what they claim is a recreation of OpenAI’s software anyway.

Aaron Gokaslan, 23, and Vanya Cohen, 24, believe their work isn’t yet harmful to society. Many would disagree, but their desire to show the world what’s possible – without being a huge company without large amounts of funding and resources – is nonetheless admirable.

“This allows everyone to have an important conversation about security, and researchers to help secure against future potential abuses,” said Cohen to WIRED. “I’ve gotten scores of messages, and most of them have been like, ‘Way to go.’”

That’s not to say their work is easy nor particularly cheap. Gokaslan and Cohen used around $50,000 worth of cloud computing from Google. However, cloud computing costs are becoming more cost-effective while increasing in power each year.

OpenAI continues to maintain its stance that such work is better off not being in the public domain until more safeguards against fake news can be put in place.

Social networks have come under pressure from governments, particularly in the West, to do more to counter fake news and disinformation. Russia’s infamous “troll farms” are often cited as being used to create disinformation and influence global affairs.

Facebook is seeking to label potential fake news using fact-checking sites like Snopes, in addition to user reports.

Last Tuesday, OpenAI released a report which said it was aware of five other groups that had successfully replicated its own software but all made the decision not to release it.

Gokaslan and Cohen are in talks with OpenAI about their work and the potential societal implications.

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Fake news is among the most difficult challenges of our time. Aside from real stories often being called it by certain politicians, actual fake news is used to coerce people into making decisions.

Governments have been putting increasing pressure on sites like Twitter and Facebook to take more responsibility for the content shared on them.

With billions of users, each uploading content, manual moderation of it all isn’t feasible. Automation is increasingly being used to flag problem content before a human moderator checks it.

Twitter CTO Parag Agrawal says its acquisition of Fabula is “to improve the health of the conversation, with expanding applications to stop spam and abuse and other strategic priorities in the future.”

Fabula has developed the ability to analyse “very large and complex data sets” for signs of network manipulation and can identify patterns that other machine-learning techniques can’t, according to Agrawal.

In addition, Fabula has created a truth-risk score to identify misinformation. The score is generated using data from trust fact-checking sources like PolitiFact and Snopes. Armed with the score, Twitter can determine how trustworthy a claim is and perhaps even make it visible to others.

A post on Twitter’s blog yesterday hints at the possible direction: “Context on Tweets and our enforcement is important in understanding our rules, so we’ll add more notices within Twitter for clarity, such as if a Tweet breaks our rules but remains on the service because the content is in the public interest.”

Often fake news is used to cause political gain or turmoil. Russia is regularly linked with modern disinformation campaigns, but even Western democracies have used it to influence both national and international affairs.

The US presidential elections were influenced by fake news. Last year, Congress released more than 3,000 Facebook ads purchased by Russian-linked agents ahead of the 2016 presidential contest.

In Fabula AI’s home country, some allege fake news was behind the UK’s decision to leave in the EU referendum. There’s less conclusive data behind the allegation, but we do know powerful targeted advertising was used to promote so-called ‘alternative facts’.

Fabula’s team will be joining the Twitter Cortex machine-learning team. Exact terms of the deal or how Fabula’s technology will be used have not been disclosed.

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The station, Q13, broadcasted a doctored Trump speech in which he somehow appeared even more orange and pulled amusing faces.

You can see a side-by-side comparison with the original below:

Following the broadcast, a Q13 employee was sacked. It’s unclear if the worker created the clip or whether it was just allowed to air.

The video could be the first DeepFake to be televised, but it won’t be the last. Social media provides even less filtration and enables fake clips to spread with ease.

We’ve heard much about sophisticated disinformation campaigns. At one point, the US was arguably the most prominent creator of such campaigns to influence foreign decisions.

Russia, in particular, has been linked to vast disinformation campaigns. These have primarily targeted social media with things such as their infamous Twitter bots.

According to Pew Research, just five percent of Americans have ‘a lot of trust’ in the information they get from social media. This is much lower than in national and local news organisations.

It’s not difficult to imagine an explosion in doctored videos that appear like they’re coming from trusted outlets. Combining the reach of social media with the increased trust Americans have in traditional news organisations is a dangerous concept.

While the Trump video appears to be a bit of fun, the next could be used to influence an election or big policy decision. It’s a clear example of how AI is already creating new threats.

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