Category: Explainability & Interpretability

Causal Effect Modeling

Causal effect modelling is a way to figure out if one thing actually causes another, rather than just being associated with it. It uses statistical tools and careful study design to separate true cause-and-effect relationships from mere coincidences. This helps researchers and decision-makers understand what will happen if they change something, like introducing a new…

Uncertainty Calibration Methods

Uncertainty calibration methods are techniques used to ensure that a model’s confidence in its predictions matches how often those predictions are correct. In other words, if a model says it is 80 percent sure about something, it should be right about 80 percent of the time when it makes such predictions. These methods help improve…

Neural Symbolic Reasoning

Neural symbolic reasoning is an approach in artificial intelligence that combines neural networks with symbolic logic. Neural networks are good at learning from data, while symbolic logic helps with clear rules and reasoning. By joining these two methods, systems can learn from examples and also follow logical steps to solve problems or make decisions.

Knowledge-Augmented Inference

Knowledge-augmented inference is a method where artificial intelligence systems use extra information from external sources to improve their understanding and decision-making. Instead of relying only on what is directly given, the system looks up facts, rules, or context from databases, documents, or knowledge graphs. This approach helps the AI make more accurate and informed conclusions,…

Causal Representation Learning

Causal representation learning is a method in machine learning that focuses on finding the underlying cause-and-effect relationships in data. It aims to learn not just patterns or associations, but also the factors that directly influence outcomes. This helps models make better predictions and decisions by understanding what actually causes changes in the data.

Uncertainty-Aware Models

Uncertainty-aware models are computer models designed to estimate not only their predictions but also how confident they are in those predictions. This means the model can communicate when it is unsure about its results. Such models are useful in situations where making a wrong decision could be costly or risky, as they help users understand…

Neural Network Interpretability

Neural network interpretability is the process of understanding and explaining how a neural network makes its decisions. Since neural networks often function as complex black boxes, interpretability techniques help people see which inputs influence the output and why certain predictions are made. This makes it easier for users to trust and debug artificial intelligence systems,…

Feature Importance Analysis

Feature importance analysis is a method used to identify which input variables in a dataset have the most influence on the outcome predicted by a model. By measuring the impact of each feature, this analysis helps data scientists understand which factors are driving predictions. This can improve model transparency, guide feature selection, and support better…

Explainable AI Strategy

An Explainable AI Strategy is a plan or approach for making artificial intelligence systems clear and understandable to people. It focuses on ensuring that how AI makes decisions can be explained in terms that humans can grasp. This helps users trust AI systems and allows organisations to meet legal or ethical requirements for transparency.