Instagram and the Dangers of Non-Transparent AI: Mechanistic Interpretability

In this episode, we explore the AI concept of mechanistic interpretability – understanding how and why an AI model makes certain decisions. Using Instagram’s machine learning-based feed ranking algorithm as an example, we discuss the dangers of algorithms that operate as black boxes. When the mechanics behind AI systems are opaque, issues like bias can go undetected. Through explaining ideas like transparency in AI and analyzing a case study on potential racial bias, we underscores why interpretable AI matters for fairness and accountability. This podcast aims to make complex AI topics approachable, relating them to real-world impacts. Join us as we navigate the fascinating intersection of technology and ethics.

This podcast was generated with the help of artificial intelligence. We do fact check with human eyes, but there might still be hallucinations in the output.

Music credit: “Modern Situations by Unicorn Heads”

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The Dangers of Non-Transparent AI: Exploring Mechanistic Interpretability Through Instagram’s Feed Algorithm


In today’s episode, we’ll be exploring the idea of mechanistic interpretability – specifically looking at why Instagram doesn’t always know why you see a certain post in your feed. This will provide insight into how AI systems make decisions, and why explaining those decisions can be incredibly complex. To start, let’s consider how Instagram works. When you open the app, you’re presented with a personalized feed of posts – photos, videos, stories – from accounts you follow. But have you ever wondered how Instagram decides the order of the posts you see? There are thousands of candidates that could appear in your feed, so how does Instagram pick the best ones to show you? Well, the answer lies in Instagram’s feed ranking algorithm…

Main Concept

In the introduction, we explored how Instagram’s machine learning-based feed ranking algorithm lacks full mechanistic interpretability – meaning the model makes accurate predictions, but the reasons behind those predictions are not entirely clear. This central concept has profound implications, so in this segment, we’ll go much deeper. To start, let’s clearly define what mechanistic interpretability means in AI systems. At its core, it refers to understanding the mechanics of how an AI model works under the hood. Having visibility into the algorithms, data and features the system uses to make decisions and predictions…

Case Study

In our last segment, we explored the ideas behind mechanistic interpretability in artificial intelligence, using Instagram’s feed ranking algorithm as an example. To make this even more concrete, let’s now walk through a real-world case study highlighting the need for transparency in AI systems. Our case focuses on Instagram’s algorithm and allegations of bias against Black users. In 2020, many Black activists and influencers spoke out about problems they faced on the platform. They noticed their content was not being shown to followers as much as it used to be…


In this episode, we’ve covered a lot of ground exploring the important AI concept of mechanistic interpretability. Let’s recap the key points: We started by looking at how Instagram’s machine learning-powered feed ranking algorithm lacks full transparency. While it shows you relevant posts, the model can’t explain the reasoning behind its curation decisions… Now looking ahead, interpretable AI remains an open challenge. But promoting transparency, fairness and human oversight is crucial as machine learning advances. We all have a role to play in advocating for responsible technology.

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