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Disinformation: Algorithmic angst


-- Oxford Analytica, Weekly Newsletter 

Faced with an increasingly well-resourced and sophisticated effort by some state actors to spread disinformation, social media companies are under growing scrutiny and pressure to get a grip on the problem.  Because of the huge volume of posts that need to be checked, reliance on technology—specifically, algorithmic filters—is inevitable. In the last few years, these tools have become better at spotting disinformation. The Fake News Challenge, which pits artificial intelligence (AI) models against each other to detect disinformation, has seen accuracy increase from around 82% in 2017 to 88% in 2019. 

Yet while the models are good at spotting fake facts, they struggle with allegations that require nuanced contextualisation. And although the technology is improving all the time, in some cases this can assist the spreader of disinformation as much as the entity trying to stop it. Language prediction models such as GPT-3 are becoming effective at summarising large volumes of text accurately, but they are also becoming better at writing like a human. 

Overreliance on AI to check disinformation has several drawbacks. The more humans rely on AI, the less their ability to check whether it is getting all the calls right. Programmer bias on grounds of race or gender is also a potential problem. AI systems can be tough to audit; they can also be subject to manipulation by those seeking to perpetuate disinformation.

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