Meta AI has peeled back the curtain to reveal the mechanisms behind its social media algorithms, aiming to clarify how Instagram and Facebook users receive content recommendations. Meta’s Global Affairs President, Nick Clegg, detailed these AI-driven systems in a recent blog post, asserting that this move aligns with Meta’s dedication to “openness, transparency, and accountability”. Furthermore, he elucidates ways users can actively manage their content preferences on the platforms. Meta AI.
An in-depth examination of Meta AI -driven customization of our social media experiences, with content selections based on our historical preferences.
Clegg acknowledged the potential opportunities and risks brought about by rapidly evolving technologies like generative AI, Meta AI emphasizing that the most effective response to such concerns is openness.
Available now are 22 “system cards” that delve into the ranking and recommendation processes for Facebook and Instagram users.
Meta AI Process:
These system cards offer insight into different aspects of content discovery on Meta’s platforms, such as Feed, Stories, Reels, etc. Each card offers a user-friendly breakdown of how AI ranks and recommends content. For instance, Instagram’s Explore card describes a three-step AI-driven process:
- Inventory Collection: The system gathers public Instagram content like photos and reels that meet the company’s quality and integrity criteria.
- Signal Utilization: The AI system considers users’ engagement with similar content or interests, or “input signals.”
- Content Ranking: Finally, the content is ranked based on the predicted interest level of the user, with higher-ranked content appearing prominently in the Explore tab.
According to the card, Instagram users can influence this process by saving content (indicating preference for similar content) or marking content as “not interested” to discourage the system from showing similar content. Additionally, users can opt for “Not personalized” to view content not specifically recommended by the algorithm. Meta’s Transparency Center offers further details on predictive Meta AI models, input signals, and content ranking frequency.
Instagram is experimenting with a feature allowing users to mark reels as “interested” to receive similar future content
In addition to system cards, the blog post highlighted other Facebook and Instagram features helping users understand and modify their content recommendations. Meta plans to extend its “Why Am I Seeing This?” feature to various areas, including Facebook Reels, Instagram Reels, and Instagram’s Explore tab in the near future. Instagram is also testing a new feature that will allow users to mark recommended reels as “Interested” to see similar content in the future. The “Not Interested” feature has been in use since 2021.
In the forthcoming weeks, Meta will start rolling out its Content Library and API, a set of tools for researchers containing public data from Instagram and Facebook. Researchers can search, explore, and filter data from this library, applying for access via approved partners, starting with the University of Michigan’s Inter-university Consortium for Political and Social Research. Meta believes these tools will offer comprehensive access to public content across Facebook and Instagram, in addition to facilitating data-sharing and transparency compliance obligations.
Transparency obligations are likely a significant driver behind Meta’s move to elucidate its AI use in content personalization. The rapid advancement and increasing popularity of AI technology have drawn worldwide regulatory attention over data collection, management, and usage concerns. The Cambridge Analytica scandal and reactions to TikTok’s lukewarm transparency efforts remind Meta to maintain clear communication. Meta AI.
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