Cybersecurity leader F-Secure has shed light on the susceptibility of AI-based recommendation systems to manipulation. These systems, particularly during major elections, face scrutiny due to potential biases that could lead to electoral interference. However, the everyday recommendations these algorithms provide hold just as much significance.
Matti Aksela, VP of Artificial Intelligence at F-Secure, emphasized the need for safeguarding AI against misuse:
“As our reliance on AI expands, understanding how to protect it from abuse is crucial. These systems, which power increasingly vital services, necessitate insights into their security strengths and weaknesses to ensure trustworthiness. Secure AI lays the groundwork for trustworthy AI.”
Disinformation campaigns, such as those initiated by Russia's notorious "troll farms," have disseminated harmful misconceptions regarding COVID-19 vaccines, immigration, and other high-profile topics.
Andy Patel, a researcher at F-Secure’s Artificial Intelligence Center of Excellence, explained:
“Social media platforms like Twitter have become arenas where diverse narratives compete. This includes organic discussions and ads, as well as deliberate misinformation aimed at undermining trust in credible information. Examining the manipulation of AI highlights its limitations and guides improvements.”
The demand for credible information is unprecedented. While skepticism is healthy, a growing number of people either doubt everything or believe indiscriminately—both of which pose significant issues.
Data from a PEW Research Center survey in late 2020 reveals that 53 percent of Americans acquire news from social media. For younger individuals, aged 18-29, social media is often the primary news source.
While no outlet is infallible, historical credibility remains vital. Tools like NewsGuard assist users in assessing this, but mainstream media typically bear more reliability than an arbitrary social media user, whose identity may be uncertain.
In 2018, research discovered that misleading Twitter posts have a 70 percent higher chance of being reshared. Such rapid redistribution underscores the swift propagation of disinformation, with platforms like Facebook now prompting users to verify information accuracy before sharing, at least for some topics like COVID-19 vaccines.
Patel trained collaborative filtering models on Twitter data to investigate these manipulations. Testing with “poisoned” datasets, he demonstrated how even minor tweaks could skew recommendation engines toward promoting accounts associated with manipulated retweets.
“Through testing simplified models, we gained insights into potential real-world attacks,” Patel noted. “Social media platforms might already be encountering attacks similar to those in our study, though they only perceive outcomes—not the underlying processes.”
Patel’s research and associated resources are accessible on GitHub.
(Photo by Charles Deluvio on Unsplash)
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