Category: Explainability & Interpretability

Feature Disentanglement

Feature disentanglement is a process in machine learning where a model learns to separate different underlying factors or features within complex data. By doing this, the model can better understand and represent the data, making it easier to interpret or manipulate. This approach helps prevent the mixing of unrelated features, so each important aspect of…

Neural-Symbolic Reasoning

Neural-symbolic reasoning is a method that combines neural networks, which are good at learning patterns from data, with symbolic reasoning systems, which use rules and logic to draw conclusions. This approach aims to create intelligent systems that can both learn from experience and apply logical reasoning to solve problems. By blending these two methods, neural-symbolic…

Model Interpretability

Model interpretability refers to how easily a human can understand the decisions or predictions made by a machine learning model. It is about making the inner workings of a model transparent, so people can see why it made a certain choice. This is important for trust, accountability, and identifying mistakes or biases in automated systems.

Neural Symbolic Integration

Neural Symbolic Integration is an approach in artificial intelligence that combines neural networks, which learn from data, with symbolic reasoning systems, which follow logical rules. This integration aims to create systems that can both recognise patterns and reason about them, making decisions based on both learned experience and clear, structured logic. The goal is to…

Knowledge Tracing

Knowledge tracing is a technique used to monitor and predict a learner’s understanding of specific topics or skills over time. It uses data from quizzes, homework, and other activities to estimate how much a student knows and how likely they are to answer future questions correctly. This helps teachers and learning systems personalise instruction to…

Explainable AI (XAI)

Explainable AI (XAI) refers to methods and techniques that make the decisions and actions of artificial intelligence systems understandable to humans. Unlike traditional AI models, which often act as black boxes, XAI aims to provide clear reasons for how and why an AI system arrived at a particular result. This transparency helps users trust and…

Causal Inference

Causal inference is the process of figuring out whether one thing actually causes another, rather than just being linked or happening together. It helps researchers and decision-makers understand if a change in one factor will lead to a change in another. Unlike simple observation, causal inference tries to rule out other explanations or coincidences, aiming…

Ghost Parameter Retention

Ghost Parameter Retention refers to the practice of keeping certain parameters or settings in a system or software, even though they are no longer in active use. These parameters may have been used by previous versions or features, but are retained to maintain compatibility or prevent errors. This approach helps ensure that updates or changes…

Intent Shadowing

Intent shadowing occurs when a specific intent in a conversational AI or chatbot system is unintentionally overridden by a more general or broader intent. This means the system responds with the broader intent’s answer instead of the more accurate, specific one. It often happens when multiple intents have overlapping training phrases or when the system…

Heuristic Anchoring Bias in LLMs

Heuristic anchoring bias in large language models (LLMs) refers to the tendency of these models to rely too heavily on the first piece of information they receive when generating responses. This bias can influence the accuracy and relevance of their outputs, especially if the initial prompt or context skews the model’s interpretation. As a result,…