Crowdsource Knowledge & Deepfakes

So far, I have explored popular digital tools that are facilitating and even advancing humanities scholarship. But the digital turn has also brought with it some problematic products of the digital age that, like forgery and plagiarism before them, can make the lives of historians, archivists, curators, and other scholars very difficult. Crowdsourcing and deepfakes are some of these matters raising concern. This post addresses both issues showing how scholars are approaching them to counteract their harmful effects.

Crowdsource knowledge refers to information provided by platforms dedicated to collecting and spreading knowledge, just like Wikipedia. Wikipedia has been a godsend for everyone who wants to know about a topic, person, country, event, and so forth. The problem is that, among that precious knowledge so easily accessed, you can also find inaccurate or misleading information; therefore, Wikipedia has become a nightmare for many educators who see it as an easy route to learn about a topic without questioning its provenance. In his article “Wikipedia as Historiographical Microcosm,” Historian Ben Wright suggests retrieving information from Wikipedia in a more active manner, learning what lies behind that information and not taking anything for granted.

In that spirit, I search Wikipedia’s page for “Digital Humanities.” And, besides reading the page content, I explored how this entry came to be. I have to admit that I have never noticed the “View History” or “Talk” buttons on the top of the page; thus, this exercise turned out to be very enlightening. I discovered that Wikipedia provides tools to examine who created the page and when it was created. Further, you can explore all the additions, corrections, and updates the page has experienced since its beginning.

I learned, for instance, Elijah Meeks, a senior data visualization engineer involved in data visualization, machine learning, and simulation, created the “Digital Humanities” page on January 30, 2006. I also learned that its contributors (at least the sample that I checked) come from a digital humanities background and have as impressive credentials as its creator. Additionally, I learned that contributors not only add information; they also make sure that the existent information is as accurate as possible.

For me, this speaks to the trustworthiness and reliability of the “Digital Humanities” entry. And I will have to say that this comes as a surprise. I am skeptical by nature; thus, I always approached Wikipedia as the last resource and took its content with a grain of salt. However, I believe that the auto police system put in place by Wikipedia is a model to follow for controlling crowdsource knowledge and protecting the integrity of information.

Unfortunately, the case of deepfakes is a bit more complicated. Deepfakes are, according to (why no) Wikipedia, “synthetic media in which a person in an existing image or video is replaced with someone else’s likeness.” This is not a new problem by any means. Faking content has always existed, but technology has advanced so much that it is hard to differentiate “real” from “fake,” which is becoming a nightmare for archivists, among many others.

Archivists have to deal with big data sorting out what should go in the historical record and what should not. As Melanie Ehrenkrantz explains in her article “How Archivists Could Stop Deepfakes From Rewriting History,” videos created using artificial intelligence are making their work much harder. Ehrenkrantz argues that “While many have feared the potential of deepfakes to spread misinformation in the here and now, these videos could distort reality long after today’s fake news goes viral if they’re improperly archived as legitimate.”

I agree with Ehrenkrantz when she states that the best way to preserve the historical record is by meticulously documenting the hoaxes as they come, which will provide guidelines to use in the future. For that, of course, it is necessary more funding, which is more than fair and reasonable. As new technology keeps coming out, total disclosure and extensive documentation might keep misleading information at bay and allow reliable scholarship keeps growing.  

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