6 – Interpretability – Machine Learning Blog | ML@CMU | Carnegie Mellon University

The objectives machine learning models optimize for do not always reflect the actual desiderata of the task at hand. Interpretability in models allows us to evaluate their decisions and obtain information that the objective alone cannot confer. Interpretability takes many forms and can be difficult

6 – Interpretability – Machine Learning Blog | ML@CMU | Carnegie Mellon University --> Input your search keywords and press Enter. Categories: Research Educational Categories: Research Educational Educational machine learning 6 – Interpretability Authors Audrey Huang , Jeffrey Li and Naveen Shankar by Audrey Huang --> Affiliations Published August 31, 2020 DOI Figure 1: Interpretability for machine learning models bridges the concrete objectives models optimize for and the real-world (and less easy to define) desiderata that ML applications aim to achieve. Introduction The ob

related reading

• The Building Blocks of Interpretabilitydistill.pub
• The Hitchhiker's Guide to Actionable Interpretabilityactionable-interpretability-guide.github.io
• Against Interpretability: a Critical Examination of the Interpretability Problem in Machine Learninglink.springer.com
• On Optimism for Interpretabilitygoodfire.ai
• 2 Interpretability – Interpretable Machine Learningchristophm.github.io
• What is the purpose of interpretability?ericjmichaud.com
• Faithful, Interpretable Model Explanations via Causal Abstraction | SAIL Blogai.stanford.edu
• An Intuitive Explanation of Sparse Autoencoders for LLM Interpretability | Adam Karvonenadamkarvonen.github.io
• A Pragmatic Vision for Interpretability — AI Alignment Forumalignmentforum.org
• In (highly contingent!) defense of interpretability-in-the-loop ML training — AI Alignment Forumalignmentforum.org
• An Extremely Opinionated Annotated List of My Favourite Mechanistic Interpretability Papers v2 — AI Alignment Forumalignmentforum.org
• Feature Visualizationdistill.pub