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Auto logic checker: Answer to the fake news problem?

Story: Crispin Cooper | 16th March 2017, 04:31 Hrs

Fake news is not news – that is, it is not in fact news, and the matter of fake news is not a recent revelation. But while fake news is a thorny problem that needs addressing in its own right, it is part of an even bigger issue too. Discourse –- the process by which humanity collectively comes to an understanding of itself, and so shapes its own future –- is fundamentally broken.
The problem begins with the school debate, a win-or-lose scenario where one party ultimately triumphs in the claim for truth. The real world is, of course, more intricate, with numerous subtleties lying between any two extremes. Yet this model persists all the way into international politics, where complex issues are reduced to soundbites. Material that arouses heated emotions within the viewer spreads faster and wider than well-considered, evidence-based argument. 
For an elected leader, a u-turn is seen as the ultimate betrayal, but for a scientist, changing views in the face of better evidence is a sign of the highest integrity. An alert reader would recognise this, but many do not and are left uninformed and angry. 
However, the very social and digital technology that is causing and spreading these problems could instead tackle the issue. 
Is it possible that arguing with an unemotional machine rather than another human would take the ego out of discussion? Being shown where your beliefs contradict themselves would surely be an immensely valuable tool for learning.
The aim of this checker is not to be the final arbiter of truth and falsehood – but to track down conflicting evidence and counterarguments. In fact, this isn’t so far from today’s internet search extended into the semantic web. 
The futuristic part is the text processing, but that’s not essential to the system: the user could instead choose ideas, beliefs and claims manually from a crowdsourced database –- or input their own – rather than the computer doing so automatically. And there are numerous examples of experimental systems like this already been built. 
Why then, are we not using automated or crowdsourced logic checking already? It turns out that building a community of people to create the supporting data is harder than building the technology. Successful online communities do exist, albeit shaped by their own agendas. FB must be the world’s largest repository of community-generated data, but the creation process is shaped by algorithms with the ultimate aim of producing advertising revenue simply by keeping the user engaged for as long as possible. 
Perhaps more interesting is Stack Exchange where communities pose and answer questions on specific topics. 
Because maintaining a reputed source of information is integral to the model, user interaction is guided by votes and reputation scores.  
Most interesting of all is Wikipedia, which despite its imperfections has succeeded in building a charitable community directed towards documentation of knowledge. 
Returning to our fictitious logic checker, two projects built on Wikipedia have already taken significant steps towards the sort of structured information necessary to support it: Wikidata could one day become the crowdsourced database mentioned above, while dbPedia attempts to extract data automatically from existing articles.
Is this the answer to all of our problems? Of course not. No tool will completely remove the underlying power structures – including, but not limited to, online community business models – that contribute to our present day situation. But they can improve the way we communicate, and that can’t be a bad thing.

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