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.

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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"

Instagram and the Dangers of Non-Transparent AI: Mechanistic Interpretability 14:42 Share

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