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Category: explainable-ai

  • Stop Asking if a Model Is Interpretable

    Stop Asking if a Model Is Interpretable Start asking what question the explanation should answer. The post Stop Asking if a Model Is Interpretable appeared first on Towards Data Science. Manuel Franco de la Peña Go to original source

  • How to Leverage Explainable AI for Better Business Decisions

    How to Leverage Explainable AI for Better Business Decisions Moving beyond the black box to turn complex model outputs into actionable organizational strategies. The post How to Leverage Explainable AI for Better Business Decisions appeared first on Towards Data Science. Rodrigo Almeida Go to original source

  • Mechanistic Interpretability: Peeking Inside an LLM

    Mechanistic Interpretability: Peeking Inside an LLM Are the human-like cognitive abilities of LLMs real or fake? How does information travel through the neural network? Is there hidden knowledge inside an LLM? The post Mechanistic Interpretability: Peeking Inside an LLM appeared first on Towards Data Science. Julian Mendel Go to original source

  • When Shapley Values Break: A Guide to Robust Model Explainability

    When Shapley Values Break: A Guide to Robust Model Explainability Shapley Values are one of the most common methods for explainability, yet they can be misleading. Discover how to overcome these limitations to achieve better insights. The post When Shapley Values Break: A Guide to Robust Model Explainability appeared first on Towards Data Science. Alon…

  • What Is a Knowledge Graph — and Why It Matters

    What Is a Knowledge Graph — and Why It Matters How structured knowledge became healthcare’s quiet advantage The post What Is a Knowledge Graph — and Why It Matters appeared first on Towards Data Science. Steve Hedden Go to original source

  • Introducing ShaTS: A Shapley-Based Method for Time-Series Models

    Introducing ShaTS: A Shapley-Based Method for Time-Series Models Why you should not explain your time-series data with tabular Shapley methods The post Introducing ShaTS: A Shapley-Based Method for Time-Series Models appeared first on Towards Data Science. Manuel Franco de la Peña Go to original source

  • I Measured Neural Network Training Every 5 Steps for 10,000 Iterations

    I Measured Neural Network Training Every 5 Steps for 10,000 Iterations Image by Pixabay.com The post I Measured Neural Network Training Every 5 Steps for 10,000 Iterations appeared first on Towards Data Science. Javier Marin Go to original source

  • Boosting Your Anomaly Detection With LLMs

    Boosting Your Anomaly Detection With LLMs The 7 emerging application patterns you should know The post Boosting Your Anomaly Detection With LLMs appeared first on Towards Data Science. Shuai Guo Go to original source

  • Explainable Anomaly Detection with RuleFit: An Intuitive Guide

    Explainable Anomaly Detection with RuleFit: An Intuitive Guide Creating interpretable rules to characterize the identified anomalies The post Explainable Anomaly Detection with RuleFit: An Intuitive Guide appeared first on Towards Data Science. Shuai Guo Go to original source

  • Grad-CAM from Scratch with PyTorch Hooks

    Grad-CAM from Scratch with PyTorch Hooks A hands-on look at an explainable AI (XAI) technique that helps reveal why a convolutional neural network (CNN) made a particular decision The post Grad-CAM from Scratch with PyTorch Hooks appeared first on Towards Data Science. Conor O’Sullivan Go to original source

  • Formulation of Feature Circuits with Sparse Autoencoders in LLM

    Formulation of Feature Circuits with Sparse Autoencoders in LLM Large Language models (LLMs) have witnessed impressive progress and these large models can do a variety of tasks, from generating human-like text to answering questions. However, understanding how these models work still remains challenging, especially due a phenomenon called superposition where features are mixed into one…

  • What I’m Updating in My AI Ethics Class for 2025

    What I’m Updating in My AI Ethics Class for 2025 What happened in 2024 that is new and significant in the world of AI ethics? The new technology developments have come in fast, but what has ethical or values implications that are going to matter long-term? I’ve been working on updates for my 2025 class…

  • Partial Dependence Plots: How to Discover Variables Influencing a Model

    Partial Dependence Plots: How to Discover Variables Influencing a Model Have you ever wondered how machine learning models are constructed? ‘Explainability of machine learning models’ and ‘machine learning… Continue reading on Towards Data Science » Mythili Krishnan Go to original source

  • Superposition: What Makes it Difficult to Explain Neural Network

    Superposition: What Makes it Difficult to Explain Neural Network When there are more features than model dimensions Introduction It would be ideal if the world of neural network represented a one-to-one relationship: each neuron activates on one and only one feature. In such a world, interpreting the model would be straightforward: this neuron fires for…

  • 100 Years of (eXplainable) AI

    100 Years of (eXplainable) AI Reflecting on advances and challenges in deep learning and explainability in the ever-evolving era of LLMs and AI governance Image by author Background Imagine you are navigating a self-driving car, relying entirely on its onboard computer to make split-second decisions. It detects objects, identifies pedestrians, and even can anticipate behavior of…

  • Making News Recommendations Explainable with Large Language Models

    Making News Recommendations Explainable with Large Language Models A prompt-based experiment to improve both accuracy and transparent reasoning in content personalization. Deliver relevant content to readers at the right time. Image by author. At DER SPIEGEL, we are continually exploring ways to improve how we recommend news articles to our readers. In our latest (offline) experiment,…