And we can see how the tendency to appeal to the macabre is one adapted to the current news environment. But while we may not be able to change the fact that audiences want fast, dystopian takes, we can still shed light on how things are about to get even worse.
My major worry is about who creates information. Since I began the Armed Conflict Location & Event Data project and started using in-country researchers and sources to compile and review all reported events of conflict and instability, we’ve seen massive shifts in how information is collected. Today, there are thousands of good sources across the world, publishing — under the most difficult of circumstances — information about how, where and when conflict incidents are occurring. To go through these daily is an extensive, tireless effort.
Moreover, a lot of the time the best of these sources can be in obscure languages, only discussed on local radio or require in-depth understanding of the groups involved. Despite this, automated and AI-based conflict data collection models increasingly feed on social messages and (primarily — if not only) English-language media. There are no existing checks on these systems, simply a hope that the truth is evident through the noise. It isn’t.
Machines can’t process conflict reporting that mentions several places, groups, victims, times or intentions; they can’t determine a report from a rumor; online communities can’t interpret them without significant bias; and platforms can’t prioritize single accurate messages over waves of repeated, exaggerated “takes” from people the world over. And yet, all these sources are increasingly pushed as fast, easy alternatives to informing the public and affected communities.
The result is not a reliable narration of conflict — it’s a list of dubious, convenient “facts.” And that this information is now entirely subject to the laws of social media attention is a travesty.
Additionally, for conflict evidence, the push toward homogenization and “open data” is a bad turn. The idea behind open data is that we can have a base level of confidence in data quality if it’s all collected and processed in the same way, removing human interference and bias from the process. But while some data — like where your tax dollars go — is perfect for transparent and open data, other information, like where ISIS is operating in Niger, is less adaptable to this process.
