Linear probe interpretability in machine learning. We establish foundational concepts such as .
Linear probe interpretability in machine learning We can group machine learning algorithms into models that are intrinsically explainable Jul 25, 2024 · Explore ways to achieve interpretability in machine learning, such as linear regression, decision trees and LIME, and learn why ML interpretability matters. Moreover, these probes cannot affect the training phase of a model, and they are generally added after training. This book is about making machine learning models and their decisions interpretable. To facilitate learning and satisfy curiosity as to why certain predictions or behaviors are created by machines, interpretability and explanations are crucial. arXiv. LIME tests what happens to the predictions when you give variations of your data into the machine learning model. Subsequently, we delve deeper into this correlation through the lens of feature importance analysis. The PDR framework provides three overarching desiderata for evaluation: predictive ac-curacy, descriptive accuracy and relevancy, with relevancy judged relative to a human audience. . org e-Print archive provides open access to a vast repository of research papers across various scientific disciplines for academic and professional use. g. Sep 19, 2024 · Non-linear probes have been alleged to have this property, and that is why a linear probe is entrusted with this task. While Explain… Jan 16, 2025 · Learning Objectives Understand the difference between model explainability and interpretability in machine learning and AI. Systematic experiments Using a linear classifier to probe the internal representation of pretrained networks: allows for unifying the psychophysical experiments of biological and artificial systems, is not limited to measuring the contrast sensitivity function of a network, and it can be used for other psychophysics. The idea is to introduce a random feature to the dataset and train a machine learning model. First, we performed a literature survey to compare the many definitions of what makes a machine learning model interpretable. By isolating layer-specific diagnostics, linear probes inform strategies for pruning, compression, and linear probes [2], as clues for the interpretation. the linear probe) is trained on an interpretability task in the activation space of layer l (hence Ml i). Final section: unsupervised probes. Interpretability is the ability to understand the overall consequences of the model and ensuring the things we predict are accurate knowledge aligned with our initial research goal. Feb 26, 2025 · Interpretability in machine learning refers to the ability to understand and explain how a model makes decisions. Abstract We analyze a dataset of retinal images using linear probes: linear regression models trained on some “target” task, using embeddings from a deep con-volutional (CNN) model trained on some “source” task as input. After exploring the concepts of interpretability, you will learn about simple A comprehensive review of mechanistic interpretability, an approach to reverse engineering neural networks into human-understandable algorithms and concepts, focusing on its relevance to AI safety. Given a model M trained on the main task (e. Python enables data professionals to address concerns about fairness and accountability in AI systems. Oct 24, 2024 · We used ridge regression based linear probes in this study. In traditional machine learning models, such as decision trees or linear regression, understanding the model's behavior is relatively straightforward due to their transparency. Dec 30, 2024 · What is Mechanistic Interpretability? Mechanistic interpretability focuses on analyzing the internal components of machine learning models, especially deep neural networks. However, this increased focus has led to considerable confusion about the notion of interpretability. While linear probes are simple and interpretable, it is unable to disentangle features distributed features that combine in a non-linear way. Jul 23, 2025 · Model interpretability refers to the ability to understand and explain how a machine learning or deep learning model makes its predictions or decisions. The definition of interpretability I like most is that given in murdoch et al. linear probes etc) can be prone to generalisation illusions. Apr 18, 2019 · Machine learning interpretability, design probe, visual ana- lytics, data visualization, interactive interfaces ACM Reference Format: Fred Hohman, Andrew Head, Rich Caruana, Robert DeLine, and Steven Significance The recent surge in interpretability research has led to confusion on numerous fronts. Self-Influence Guided Data Reweighting for Language Model Pre-training] - An application of training data attribution methods to re-weight Dec 19, 2022 · The notion of ‘interpretability’ of artificial neural networks (ANNs) is of growing importance in neuroscience and artificial intelligence (AI). Post-hoc interpretation is the attempt to interpret potentially complex models after they were trained. With interpretable models, organizations can build more reliable and ethical AI applications that users can trust and understand. Finally, good probing performance would hint at the presence of the said property, which has the potential of being used in making final decisions to choose a label in the farthest layer of the neural network. Nov 21, 2020 · A broad overview of the sub-field of machine learning interpretability; conceptual frameworks, existing research, and future directions. Information-theoretic approaches are also used in interpretability research [Voita and Titov, 2020] and can help overcome the shortcomings of traditional linear probes by reducing reliance on linear decodability. We analyze the performance Abstract Understanding AI systems’ inner workings is critical for ensuring value alignment and safety. Our method uses linear classifiers, referred to as “probes”, where a probe can only use the hidden units of a given intermediate layer as discriminating features. 2019) and notes from this interpretable ml book (molnar 2019). We propose a new method to understand better the Academic and industry papers on LLM interpretability. However, several techniques exist for enhancing the degree of interpretability in machine learning models, regardless of their type. For interpretability specific to transformers, see here. É Probes cannot tell us about whether the information that we identify has any causal relationship with the target model’s behavior. As machine learning models become more complex and pervasive in critical decision-making processes, improving their interpretability is crucial for transparency, accountability, and trust. We analyze the performance This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners. Jul 5, 2024 · The field of machine learning has seen remarkable advancements, yet the complexity of many models has led to a significant challenge: interpretability. But interpretability means different things to A source of valuable insights, but we need to proceed with caution: É A very powerful probe might lead you to see things that aren’t in the target model (but rather in your probe). Probes in the above sense are supervised To oversimplify, the field of interpretable machine learning knows two paths: interpretability-by-design and post-hoc interpretability. Through quantitative analysis of probe performance and LLM response uncertainty across a series of tasks, we find a strong correlation: improved probe performance consistently corresponds to a reduction in response uncertainty, and vice versa. Oct 10, 2024 · Learn the key differences between interpretability and explainability in AI and machine learning, and explore examples, techniques and limitations. For example, simple probes have shown language models to contain information about simple syntactical features like Part of Speech tags, and more complex probes have shown models to contain entire Parse trees of sentences. Jan 26, 2022 · Moreover, the applications of machine learning in highly regulated and critical domains like criminal justice, financial services, and healthcare require measuring machine learning models not only based on their accuracy but also their interpretability and transparency. Your goal is to understand why the machine learning model made a certain prediction. LIME Local Interpretable Model-agnostic Explanations (LIME) is a framework and technique designed to provide interpretability and insights into the predictions of complex machine learning models. In We propose Deep Linear Probe Generators (ProbeGen) for learning better probes. Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. We aim to clarify these concerns by defining interpretable machine learning and constructing a unifying framework for Feb 5, 2024 · Interpretable Machine Learning Methods In artificial intelligence, we have a wide range of algorithms, from linear regression to neural networks, that vary in complexity and hence, interpretability. DNN trained on im-age classification), an interpreter model Mi (e. Although interpretability and explainability have escaped a precise and universal definition, many models and techniques motivated by these properties have been developed Linear-Probe Classification: A Deep Dive into FILIP and SODA | SERP AIhome / posts / linear probe classification Some interesting papers on interpretable machine learning, largely organized based on this interpretable ml review (murdoch et al. We establish foundational concepts such as Oct 21, 2023 · 2. In particular, it is unclear what it means to be interpretable and how to select, evaluate, or even discuss methods for producing interpretations of machine-learning models. This review explores mechanistic interpretability: reverse-engineering the computational mechanisms and representations learned by neural networks into human-understandable algorithms and concepts to provide a granular, causal understanding. The authors state interpretability is required when a problem formulation is incomplete, when the optimization problem – the key defi-nition to solve the majority of machine learning problems – is disconnected from evaluation. Interpretability in Machine Learning Interpretability in machine learning (5:59) Importance of interpretability (8:54) Interpretability methods (7:44) Global and local explanations (5:27) Challenges to interpretability (7:54) Making models more interpretable (7:37) How are we doing? (0:26) Additional reading resources Quiz Linear regression When opaque machine learning models are used in research, scientific findings remain completely hidden if the model only gives predictions without explanations. Evaluating Interpretability [Doshi-Velez 2017] Application level evaluation – Put the model in practice and have the end users interact with explanations to see if they are useful . 2019, which states that interpretability needs to be grounded in About the Book Summary Machine learning is part of our products, processes, and research. Aug 17, 2019 · Earlier machine learning methods for NLP learned combinations of linguistically motivated features—word classes like noun and verb, syntax trees for understanding how phrases combine, semantic labels for understanding the roles of entities—to implement applications involving understanding some aspects of natural language. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. Aug 31, 2020 · 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. 5. Jun 25, 2019 · Researchers currently must compensate for incomplete interpretability with judgement, experience, observation, monitoring, and diligent risk management–including a thorough understanding of the datasets they use. After training the ML model, extract the feature importances. Apr 1, 2017 · Request PDF | Understanding intermediate layers using linear classifier probes | Neural network models have a reputation for being black boxes. Mar 13, 2025 · The need for interpretability grows as machine learning impacts more areas of society. Interpretability-by-design is about only using interpretable, aka simple models. But interpretability means different things to Mar 28, 2023 · Omg idea! Maybe linear probes suck because it's turn based - internal repns don't actually care about white or black, but training the probe across game move breaks things in a way that needs smth non-linear to patch At this point my instincts said to go and validate the hypothesis properly, look at a bunch more neurons, etc. Interpretability Illusions in the Generalization of Simplified Models – Shows how interpretability methods based on simplied models (e. Explore the importance of explainability and interpretability in building trust in AI systems. May 14, 2025 · What are probing classifiers and can they help us understand what’s happening inside AI models? - Blog post by Sarah Hastings-Woodhouse Mechanistic interpretability (often abbreviated as mech interp, mechinterp, or MI) is a subfield of research within explainable artificial intelligence that aims to understand the internal workings of neural networks by analyzing the mechanisms present in their computations. You can probe the box as often as you want. As such, from the early days of deep learning, there have been efforts to explain these models’ behavior and understand them inter-nally; and recently, mechanistic interpretability (MI) has emerged as a distinct research area studying the features and implicit algorithms learned by foundation models such as large lan-guage models. Since many AI models, especially deep learning models, operate as "black boxes," it is crucial to ensure transparency for trust, debugging, and regulatory compliance. May 24, 2025 · Through quantitative analysis of probe performance and LLM response uncertainty across a series of tasks, we find a strong correlation: improved probe performance consistently corresponds to a reduction in response uncertainty, and vice versa. Interpretability in models allows us to evaluate their decisions Sep 30, 2023 · The Probe method is a highly intuitive approach to feature selection. This document is part of the arXiv e-Print archive, featuring scientific research and academic papers in various fields. We aim to address these concerns by defining interpretability in the context of machine learning and introducing the Predictive, Descriptive, Relevant (PDR) framework for discussing interpreta-tions. Understanding how and why a model makes certain decisions is crucial, especially in high-stakes domains like healthcare, finance, and autonomous driving. Feb 28, 2023 · Interpretability and explainability are crucial for machine learning (ML) and statistical applications in medicine, economics, law, and natural sciences and form an essential principle for ML model design and development. There is a trade-off between complexity and interpretability, and I will say more about in coming paragraphs. Dec 19, 2022 · The notion of ‘interpretability’ of artificial neural networks (ANNs) is of growing importance in neuroscience and artificial intelligence (AI). May 1, 2025 · The increasing use of Deep Learning (DL) in healthcare has highlighted the critical need for improved transparency and interpretability. Assessing the Probe’s Features We took two approaches to design a visualization system to probe machine learning interpretability. But computers usually don’t explain their predictions, which can cause many problems, ranging from trust issues to undetected bugs. 9. Learn how LIME and SHAP tools enhance model transparency and decision-making insights. We used Gamut as a design probe during an in-lab study to understand how data scientists understand machine learning models and answer interpretability questions. This article explores the importance of interpretable machine learning models, various Jul 23, 2025 · Model interpretability refers to the ability to understand and explain how a machine learning or deep learning model makes its predictions or decisions. Introduction The objectives machine learning models optimize for do not always reflect the actual desiderata of the task at hand. This random feature is understand to have no useful information to predict the Y. They reveal how semantic content evolves across network depths, providing actionable insights for model interpretability and performance assessment. Moreover, to Jul 23, 2025 · Interpretability in machine learning refers to the ability to understand and explain the predictions and decisions made by models. In addition to the generalizing networks trained on correct data, two types of intentionally flawed models are used for Dec 16, 2024 · These probes can be designed with varying levels of complexity. Nov 4, 2023 · Mechanistic Interpretability is a field of study that concerns study of neural networks (more generally ML models) with an intent to understand and explain inner workings of a machine learned model. ProbeGen op-timizes a deep generator module limited to linear expressivity, that shares information between the different probes. Jan 14, 2019 · Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. 2 Interpretability-by Mar 28, 2023 · Omg idea! Maybe linear probes suck because it's turn based - internal repns don't actually care about white or black, but training the probe across game move breaks things in a way that needs smth non-linear to patch At this point my instincts said to go and validate the hypothesis properly, look at a bunch more neurons, etc. We use this method across all possi-ble pairings of 93 tasks in the UK Biobank dataset of retinal images, leading to 164k different mod-els.