Bilgesu Erdem
Wikipedia, the world’s leading online encyclopedia, relies heavily on its references – links to sources that authenticate the information on its pages. Nevertheless, these references sometimes mislead, directing to inaccurate sources, unreliable information, or broken links.
Introducing SIDE
Research recently published in Nature Machine Intelligence1 offers a promising solution. A neural-network-based system named SIDE, developed by Fabio Petroni at the London-based company Samaya AI and his team, aims to enhance Wikipedia’s citation integrity. By examining whether Wikipedia’s references genuinely support the claims they are linked to, SIDE can suggest more credible alternatives.
Utilizing a training set comprised of Wikipedia’s featured articles – articles recognized and promoted due to their accuracy and thoroughness – SIDE hones its capability to discern good references. It is adept at pinpointing claims with inferior-quality citations, scanning the web for reputable substitutes, and ranking them based on relevance.
Testing the System
When the research team unleashed SIDE on featured Wikipedia articles outside its training set, the results were encouraging. In nearly half of the instances, the AI’s top recommendation for a citation was already present in the article. For the remaining, SIDE identified alternative references. When these findings were presented to Wikipedia users, 21% favored the AI-generated citations, 10% chose the original ones, while a substantial 39% expressed no preference.
Aleksandra Urman, a computational communication scientist at the University of Zurich, underscores the potential efficiency the tool can offer Wikipedia’s editors and moderators. However, Urman mentions an intriguing observation: Wikipedia users testing the SIDE system were twice as likely to reject both the original and AI-suggested references, indicating they might opt to conduct independent online searches for reliable citations.
The AI Limitation and Bias Debate
Yet, SIDE’s promising efficiency is accompanied by certain limitations. One of the constraints is its scope; it predominantly considers web page references, leaving out diverse sources like books, scientific journals, and multimedia content.
Beyond technical limitations, the researchers pointed out a potential bias challenge. Wikipediaโs decentralized structure means anyone can add a reference. This could introduce a biased viewpoint depending on the topic and the contributor’s perspective. The AI’s learning process could also be skewed by the programmer’s biases or the data used for training, which might limit SIDEโs potential benefits.
Digital platforms, like Wikipedia and various social media, grapple with the challenge of misinformation. With significant events on the horizon, like the upcoming US presidential elections and the backdrop of the Israel-Hamas conflict, accurate information dissemination is crucial. AI tools, epitomized by SIDE, could serve as a significant ally in combating misinformation. However, further refinements are necessary to fully harness their potential.
