AI and Fake News in Social Media
By: Ani Aggarwal
Tags: Supervised Learning, Social Media, Fake News, AI, Unsupervised Learning

Image courtesy of Lawtomated; edit by Ani Aggarwal

For Unsupervised Learning

Author: Dhanin Wongpanich

Disinformation is quickly becoming one of the largest problems we face in this decade, playing a critical role in everything from political elections to the spread of the Covid-19 pandemic. In our fight against this issue, artificial intelligence (AI) has emerged as a powerful weapon that can detect disinformation. Currently, researchers are pursuing supervised learning algorithms that rely on human annotated datasets. However, the more unconventional approach of unsupervised learning promises to draw from the strengths of both AI and humans, which ultimately results in less bias, better performance and new insights.

One of the primary advantages of unsupervised models is how they are created. Data is one of the most important parts of this process. In supervised learning, the AI learns by looking at human annotated data, which often consists of chunks of text or news articles that are accompanied by human commentary. After this process, the AI can learn to sort data it has never seen before. Unsupervised learning presents us with a different approach. Instead of using human annotated data, it simply takes in the raw data — the news articles and chunks of text alone — and uncovers natural patterns in the data. This has huge advantages. It avoids the expensive and time-consuming process of human annotation and also helps to reduce human errors. Human annotations will often have noisy and incorrect annotations which greatly reduce the effectiveness of supervised learning and the resulting AI (Soni). Furthermore, by forgoing the expensive process of annotating data, unsupervised datasets can be much larger. The effect of this is twofold. More data allows for models to be more generalized, and each potential error in the dataset has a smaller effect, says Apoorv Agarwal, cofounder of Text IQ, a startup focused on privacy and security in AI systems (Kahn). Alternatively, the lower cost of producing an unsupervised dataset can also be used in another way — creating more up-to-date datasets. Because of the high cost and time commitment that creating an annotated dataset requires, most annotated datasets are many years old, and may not be representative of what current disinformation looks like. Combined with bad actors who attempt to defeat these automated systems, it is almost impossible for these outdated annotated datasets to accurately represent the current situation. This disconnect between the learning data and the real world is one of the main reasons why promising AI based systems fail to deliver in practical applications (Heaven). Bridging this gap can be done in a variety of ways; however, one of the simplest and most effective is to create a new dataset. Should we really spend our effort creating expensive supervised datasets that will soon be obsolete, when cheaper and arguably better unsupervised datasets could be used instead?

Furthermore, one of the most important issues plaguing AI is systematic bias. Although it is hard to imagine an AI caring about race or gender, AI has already shown to be tainted by our systematic biases. In one case, tweets by self-identified African Americans were 1.5 times more likely to be flagged as offensive by Twitter’s automated detection algorithm. Thomas Davidson, a researcher from Cornell University, suggests that the problem is with the data. He remarks, “You can have the most sophisticated neural network model, but the data is biased because humans are deciding what’s hate speech and what’s not” (Ghaffary). This highlights an inherent flaw in supervised learning based AI that results in the entrenchment of historical biases which further hurt people of color and other marginalized groups. Although it is impossible to completely eliminate all bias, unsupervised learning is a step in the right direction. Unsupervised learning forgoes human annotation, which means that human biases are less likely to transfer into the AI. Yann LeCun, a recipient of the prestigious Turing Award — often called the Nobel Prize of computing — agrees. He remarks that “self-supervised systems were… less likely to be biased than some AI software that learns from labeled examples… because labels are often applied by biased humans and the data sets were smaller, so each biased example would have a bigger impact” (Kahn). Despite this, unsupervised models are not without their issues. Unsupervised learning still suffers from biases in data composition, and because of the massive size of unsupervised datasets, it is often impractical to audit. Ultimately, unsupervised systems still suffer from human biases, but cutting out human annotations takes a valuable step forward in reducing bias.

Another key advantage to unsupervised models is their ability to create their own intuitions. Without relying on human annotations, unsupervised models are forced to create their own ways of sorting the data which is often different from humans. Although this initially seems like a disadvantage, it is actually beneficial because when used in conjunction with human moderators, they can detect fake news that could slip past humans. This is considered a common strength of unsupervised learning based AI, where it is often used to detect patterns and trends humans cannot see (Soni). Furthermore, because of unsupervised learning’s lower cost, unsupervised models tend to be more advanced and can introduce new ideas without requiring the creation of new, expensive datasets. In one study, an unsupervised model was able to consider “text content, images, propagation information, and user information of publishing news”, something that would be extremely difficult with the limited supervised datasets that currently exist. This more holistic approach improves accuracy, and the new insights that arise following the development of unsupervised models could pave the way for the future of AI. Unsupervised learning’s potential for rapid development and separation from human-introduced biases makes it a powerful tool to defeat disinformation.

When we consider the benefits of unsupervised learning, it is undeniable that it contributes less bias and allows us to work with more robust datasets. It is not a matter of if we should switch to supervised learning, but when. Although supervised learning may be the AI of today, unsupervised learning will be the AI of tomorrow.