Quicksilver: the AI that helps Wikipedia give a voice to women scientists

Joëlle Pineau is a Canadian roboticist and was one of the first researchers to apply the scientific rigor to IA systems; a few months ago she was appointed head of the Facebook’s new artificial intelligence Research lab in Montreal. Miriam Adelson is a famous addiction treatment researcher, and also one of the biggest investors in her field. Evelyn Wang became director of the prestigious MechE department of MIT, acclaimed in recent months for some discoveries, such as the one concerning the invention of a device that can generate drinking water by exploiting sunlight and air in the desert.

Despite the results of these three women are already making history in the field of scientific innovation, until a few weeks ago none of them had their own dedicated page on Wikipedia. The problem does not only concern the aforementioned ladies: the most visited online source of information in the world, in fact, often ends up ignoring important personalities of the scientific community, especially when it comes to women.

To make up for this problem, a new artificial intelligence system has been created: Quicksilver. Launched by the AI startup Primer, presented by scientific director John Bohannon in a blog post on the company website, Quicksilver promises to fix the Wikipedia bias regarding underrated scientists. But how does this system work?

Primer’s team has trained the AI by providing it with tons of information, starting with the Wikipedia entries regarding 30,000 scientists and a total of over 3 million sentences taken from news stories about scientists and their work. Subsequently, data from over 200,000 people who wrote scientific articles were provided to Quicksilver. In a single day, the system determined that of these 40,000 did not have an entry on Wikipedia, despite having received the same media coverage of scientists who had one. Furthermore, the AI was able to retrieve information missing from already published pages.

Not only that: Quicksilver has also been designed to write Wikipedia-style entries about scientists who don’t appear on the online encyclopedia, using the information available on the Internet. Primer has already made 100 of these entries available, in the hope that someone will pick up the signal and decide to continue the work started by Quicksilver.

“Wikipedia is incredibly biased and the underrepresentation of women in science is particularly bad,” says Jessica Wade, a physicist at Imperial College London who personally added nearly 300 women scientists to the site over the past year. “With Quicksilver, you don’t have to trawl around to find missing names, and you get a huge amount of well-sourced information very quickly.”

“It’s hard to grasp just how important Wikipedia has become for the world, and how vulnerable. It is the fifth most visited website, serving more than 15 billion page views per month. It includes nearly 50 million articles, written in almost 300 languages—only 13% in English. It boggles the mind that all of this is created by human volunteers.” Bohannon explains in his blog post. “But with human hands come human limitations. As it becomes more and more essential to the world, biased and missing information on Wikipedia will have serious impacts. The human editors of the most important source of public information can be supported by machine learning. Algorithms are already used to detect vandalism and identify underpopulated articles. But the machines can do much more. They can track and summarize information missing from Wikipedia articles. They can even identify articles that are missing altogether, and generate the first draft.”

Quicksilver, in short, is yet another proof that technology is a useful tool to fill the gaps in human knowledge, but also that to really solve this problem we need the “human superpower”.