Meta, the parent company of Facebook and Instagram, has undertaken a momentous endeavor to demystify the mechanisms behind its AI algorithms that determine the content presented to users on these platforms. In a blog post released on Thursday, Nick Clegg, Meta’s President of Global Affairs, elucidated that this information release on the AI systems underpinning their algorithms is an embodiment of the company’s “wider ethos of openness, transparency, and accountability.” Furthermore, Clegg outlined the measures Facebook and Instagram users can take to exert greater control over the content they encounter on these social media platforms.
Recognizing the tremendous strides made in cutting-edge technologies such as generative AI, Clegg acknowledged the amalgamation of excitement and apprehension that accompanies these advancements. He affirmed, “We firmly believe that the most effective response to these concerns lies in embracing openness.”
The crux of this disclosure resides within 22 comprehensive “system cards,” which extensively cover the various aspects of Meta’s social media platforms, including the Feed, Stories, Reels, and other avenues through which users explore and consume content. Each of these cards offers meticulous yet accessible insights into the functioning of the AI systems driving these features’ content ranking and recommendation processes. For instance, an intricate examination of Instagram Explore, a feature that showcases users photos and reels content from accounts they don’t follow, reveals a three-step process constituting the foundation of the automated AI recommendation engine.
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Firstly, the system accumulates a wealth of public Instagram content, such as photos and reels, that adheres to the company’s quality and integrity guidelines. Subsequently, the AI system leverages input signals derived from user interactions with similar content or shared interests. Lastly, the system ranks the gathered content based on its predictions of user interest, subsequently assigning a higher position to content deemed more captivating. The system card further elucidates that Instagram users have the capacity to influence this process by saving content, signaling their preference for similar content, or marking it as “not interested” to prompt the system to filter out similar content in the future. Additionally, users can opt to view reels and photos that have not been specifically tailored for them by selecting “Not personalized” in the Explore filter. For a more comprehensive understanding of Meta’s predictive AI models, the input signals utilized, and the frequency of their implementation in content ranking, users can access the Transparency Center.
In conjunction with the system cards, the blog post highlights several other Instagram and Facebook features that enable users to comprehend the rationale behind the content they encounter and tailor their recommendations accordingly. In the upcoming weeks, Meta plans to extend the “Why Am I Seeing This?” feature to encompass Facebook Reels, Instagram Reels, and the Instagram Explore tab. This expansion will empower users to click on individual reels to gain insights into how their prior activities may have influenced the system’s decision to present the content to them. Additionally, Instagram is currently testing a novel Reels feature that allows users to mark recommended reels as “Interested” to facilitate the discovery of similar content in the future. Since 2021, the option to mark content as “Not Interested” has been available.
Moreover, Meta has announced the imminent rollout of its Content Library and API, a comprehensive suite of tools designed for researchers. These tools will contain an extensive collection of public data from Instagram and Facebook, which can be searched, explored, and filtered. Researchers will have the opportunity to apply for access to these tools through approved partners, beginning with the University of Michigan’s Inter-university Consortium for Political and Social Research. Meta asserts that these tools will provide researchers with unparalleled access to publicly available content across Facebook and Instagram, significantly aiding the company in fulfilling its obligations regarding data sharing and transparency.
Undoubtedly, these transparency obligations constitute a significant driving force behind Meta’s decision to elucidate the intricate manner in which AI shapes and influences the content we encounter and engage with on their platforms. The remarkable advancements in AI technology, coupled with its surge in popularity over recent months, have drawn the attention of regulators worldwide, who harbor concerns regarding the collection, management, and utilization of personal data by these systems. While Meta’s algorithms are not novel, the mishandling of user data during the Cambridge Analytica scandal, along with the lukewarm transparency efforts by TikTok, likely serve as stark reminders of the imperative to over-communicate and foster enhanced user understanding and control.
