Interpretability on English AI Terms Dictionary

Rule induction

Sat, 18 Jul 2026 10:14:22 +0000

Definition

Rule induction is a symbolic machine learning method that derives if-then rules directly from data. Unlike neural networks, which produce opaque weights, rule induction yields interpretable models consisting of explicit conditions and conclusions. Algorithms search for patterns that best separate classes, creating a decision list or set of rules. This approach is valued for its transparency and ease of understanding, making it suitable for domains requiring clear justification for decisions.

Polysemanticity

Sat, 18 Jul 2026 10:10:59 +0000

Definition

Polysemanticity is a characteristic observed in deep neural networks, particularly in transformers, where a single neuron may activate in response to several unrelated or semantically distinct features. This contrasts with monosemantic neurons, which respond to only one specific concept. Understanding polysemanticity is crucial for interpretability research, as it complicates efforts to map specific network components to human-understandable concepts, necessitating advanced techniques like sparse autoencoders for disentanglement.

Summary

The phenomenon where individual neurons in neural networks respond to multiple distinct concepts.

Owain Evans

Sat, 18 Jul 2026 10:10:06 +0000

Definition

Owain Evans is a computer scientist and educator, currently associated with the Center for AI Safety and previously with Anthropic. He is widely recognized for his contributions to mechanistic interpretability, focusing on understanding how neural networks internally represent information. His research often involves designing benchmarks to test whether LLMs truly understand concepts or merely mimic patterns. He also creates educational content to help the community understand complex AI safety and alignment topics.

Neuro-symbolic AI

Sat, 18 Jul 2026 10:08:54 +0000

Definition

Neuro-symbolic AI integrates sub-symbolic neural learning methods with symbolic logic-based reasoning systems. This hybrid approach aims to overcome the limitations of pure deep learning, such as lack of interpretability and poor generalization from few examples, by incorporating explicit knowledge structures. It enables systems to learn from data while maintaining logical consistency and providing explainable decisions through rule-based inference.

Summary

An AI approach combining neural networks’ learning capabilities with symbolic reasoning’s logic and transparency.

Mechanistic interpretability

Sat, 18 Jul 2026 10:06:58 +0000

Definition

Mechanistic interpretability focuses on reverse-engineering neural networks to understand how they compute specific functions at the level of individual neurons, weights, and circuits. Instead of treating the model as a black box, researchers map out the causal pathways and logical structures within the network. This field aims to identify interpretable features and algorithms implemented by the model, providing insights into how complex behaviors emerge from simple mathematical operations, thereby enhancing safety and controllability.

Explainable artificial intelligence

Sat, 18 Jul 2026 09:57:38 +0000

Definition

As machine learning models become more complex, particularly deep neural networks, their decision-making processes often become opaque ‘black boxes.’ XAI aims to make these decisions interpretable and transparent to humans. This is crucial for building trust, ensuring fairness, complying with regulations like GDPR, and debugging models. Techniques include feature importance analysis, LIME, SHAP, and attention mechanisms, which help users understand why a specific prediction was made.

Summary

Explainable AI (XAI) refers to methods and techniques in the application of artificial intelligence technology such that the results of the solution can be understood by human experts.

ExBERT

Sat, 18 Jul 2026 09:57:25 +0000

Definition

ExBERT provides interpretability for the BERT transformer model by analyzing the importance of individual attention heads across different layers. It uses techniques like gradient-based attribution or ablation studies to determine which parts of the model are responsible for specific token predictions or semantic features. This helps researchers understand how BERT processes linguistic information and debugs model behavior in natural language processing tasks.

Summary

A method for explaining BERT’s predictions by identifying which attention heads and layers contribute most to specific outputs.

Decision list

Sat, 18 Jul 2026 09:55:14 +0000

Definition

A decision list is a type of machine learning model that represents knowledge as a sequence of conditional rules. Each rule consists of a condition and a predicted class label. When classifying a new instance, the model evaluates the rules in order and returns the label associated with the first rule whose condition is satisfied. This structure offers high interpretability compared to complex neural networks, making it useful for domains requiring transparent decision-making processes.

Class activation mapping

Sat, 18 Jul 2026 09:49:31 +0000

Definition

CAM generates heatmaps overlaid on input images to show which pixels contributed most to the model’s decision for a particular class label. It works by applying global average pooling to the final convolutional feature maps, weighted by the importance of each map for the target class. This technique enhances model interpretability, allowing developers to debug biases, verify that models focus on relevant features rather than artifacts, and build trust in computer vision applications.

black-box

Sat, 18 Jul 2026 09:38:06 +0000

Definition

In AI, a black-box model refers to complex systems like deep neural networks where the internal decision-making logic is opaque and difficult for humans to interpret. While these models often achieve high predictive accuracy, their lack of transparency poses challenges for debugging, regulatory compliance, and trust, leading to the field of Explainable AI (XAI) which seeks to uncover their internal reasoning processes.

Summary

A system where internal mechanisms are hidden, and only inputs and outputs are observable.

Understanding

Sat, 18 Jul 2026 09:37:37 +0000

Definition

AI understanding goes beyond statistical correlation to interpret the underlying meaning of data. For language models, this involves grasping syntax, semantics, and pragmatics to generate coherent and relevant responses. While current systems simulate understanding through complex pattern recognition in high-dimensional spaces, true semantic comprehension remains a subject of debate regarding whether models possess genuine intent or merely mimic human-like reasoning based on vast training corpora.

Summary

In AI, the ability of a model to comprehend semantic meaning, context, and intent within input data rather than just pattern matching.