Mechanistic interpretability aims to understand the internal workings of AI systems to ensure safety and alignment with human values. This review explores mechanistic interpretability, an approach that seeks to reverse-engineer the computational mechanisms and representations learned by neural networks into human-understandable algorithms and concepts. It emphasizes the importance of understanding these complex systems as a societal imperative to ensure AI remains trustworthy and beneficial. The field of interpretability is undergoing a shift from surface-level analysis to a focus on the internal mechanics of deep neural networks. Mechanistic interpretability, as an approach to inner interpretability, aims to completely specify a neural network's computation, potentially in a format as explicit as pseudocode, striving for a granular and precise understanding of model behavior. It distinguishes itself through its ambition for comprehensive reverse engineering and its strong motivation towards AI safety. The review discusses foundational concepts such as features encoding knowledge within neural activations and hypotheses about their representation and computation. It surveys methodologies for causally dissecting model behaviors and assesses the relevance of mechanistic interpretability to AI safety. It examines benefits in understanding, control, alignment, and risks such as capability gains and dual-use concerns. It investigates challenges surrounding scalability, automation, and comprehensive interpretation. The review also explores the nature of features, including the challenges posed by polysemantic neurons and the implications of the superposition and linear representation hypotheses. It discusses the implications for understanding emergent properties, such as internal world models and simulated agents with potentially misaligned objectives. The review highlights the importance of identifying the fundamental units of neural networks, which are called features, and the role of circuits and motifs in computational processes. The review presents a taxonomy of mechanistic interpretability methods, categorizing approaches based on their key characteristics. It surveys observational methods, including example-based and feature-based methods, and interventional techniques. It studies their synergistic interplay and provides a visual summary of the methods and techniques unique to mechanistic interpretability. Overall, mechanistic interpretability could help prevent catastrophic outcomes as AI systems become more powerful and inscrutable. It is a critical area of research for ensuring AI safety and alignment with human values.Mechanistic interpretability aims to understand the internal workings of AI systems to ensure safety and alignment with human values. This review explores mechanistic interpretability, an approach that seeks to reverse-engineer the computational mechanisms and representations learned by neural networks into human-understandable algorithms and concepts. It emphasizes the importance of understanding these complex systems as a societal imperative to ensure AI remains trustworthy and beneficial. The field of interpretability is undergoing a shift from surface-level analysis to a focus on the internal mechanics of deep neural networks. Mechanistic interpretability, as an approach to inner interpretability, aims to completely specify a neural network's computation, potentially in a format as explicit as pseudocode, striving for a granular and precise understanding of model behavior. It distinguishes itself through its ambition for comprehensive reverse engineering and its strong motivation towards AI safety. The review discusses foundational concepts such as features encoding knowledge within neural activations and hypotheses about their representation and computation. It surveys methodologies for causally dissecting model behaviors and assesses the relevance of mechanistic interpretability to AI safety. It examines benefits in understanding, control, alignment, and risks such as capability gains and dual-use concerns. It investigates challenges surrounding scalability, automation, and comprehensive interpretation. The review also explores the nature of features, including the challenges posed by polysemantic neurons and the implications of the superposition and linear representation hypotheses. It discusses the implications for understanding emergent properties, such as internal world models and simulated agents with potentially misaligned objectives. The review highlights the importance of identifying the fundamental units of neural networks, which are called features, and the role of circuits and motifs in computational processes. The review presents a taxonomy of mechanistic interpretability methods, categorizing approaches based on their key characteristics. It surveys observational methods, including example-based and feature-based methods, and interventional techniques. It studies their synergistic interplay and provides a visual summary of the methods and techniques unique to mechanistic interpretability. Overall, mechanistic interpretability could help prevent catastrophic outcomes as AI systems become more powerful and inscrutable. It is a critical area of research for ensuring AI safety and alignment with human values.