Mel-Frequency Cepstral Coefficients (MFCCs)
Ethics & Safety
Reverse-engineering neural networks to understand their internal computations and algorithms.
Detailed Explanation
Mechanistic interpretability involves analyzing neural networks at a granular level to uncover how specific components and pathways produce particular outputs. By reverse-engineering these models, researchers aim to understand the internal computations, decision-making processes, and algorithms, which helps identify potential biases, improve transparency, and ensure safe, aligned AI behavior, fostering trust and accountability in AI systems.
Use Cases
- •Use case: Auditing AI models to detect biases and ensure safety by understanding their internal decision-making processes.
Related Terms
Other terms in the Ethics & Safety category