Category: Explainability & Interpretability

Neural Activation Analysis

Neural activation analysis is the process of examining which parts of a neural network are active or firing in response to specific inputs. By studying these activations, researchers and engineers can better understand how a model processes information and makes decisions. This analysis is useful for debugging, improving model performance, and gaining insights into what…

Neural Calibration Metrics

Neural calibration metrics are tools used to measure how well the confidence levels of a neural network’s predictions match the actual outcomes. If a model predicts something with 80 percent certainty, it should be correct about 80 percent of the time for those predictions to be considered well-calibrated. These metrics help developers ensure that the…

Feature Correlation Analysis

Feature correlation analysis is a technique used to measure how strongly two or more variables relate to each other within a dataset. This helps to identify which features move together, which can be helpful when building predictive models. By understanding these relationships, one can avoid including redundant information or spot patterns that might be important…

Symbolic Reasoning Integration

Symbolic reasoning integration is the process of combining traditional logic-based reasoning methods with modern data-driven approaches like machine learning. This integration allows systems to use explicit rules and symbols, such as if-then statements or mathematical logic, alongside statistical learning. The goal is to create smarter systems that can both learn from data and apply clear,…

Causal Knowledge Integration

Causal knowledge integration is the process of combining information from different sources to understand not just what is happening, but why it is happening. This involves connecting data, theories, or observations to uncover cause-and-effect relationships. By integrating causal knowledge, people and systems can make better predictions and decisions by understanding underlying mechanisms.

Uncertainty-Aware Inference

Uncertainty-aware inference is a method in machine learning and statistics where a system not only makes predictions but also estimates how confident it is in those predictions. This approach helps users understand when the system might be unsure or when the data is unclear. By quantifying uncertainty, decision-makers can be more cautious or seek additional…

Blockchain-Based Model Auditing

Blockchain-based model auditing uses blockchain technology to record and verify changes, decisions, and actions taken during the development and deployment of machine learning or artificial intelligence models. This creates a secure and tamper-proof log that auditors can access to check who made changes and when. By using this approach, organisations can improve transparency, accountability, and…

Neural Calibration Frameworks

Neural calibration frameworks are systems or methods designed to improve the reliability of predictions made by neural networks. They work by adjusting the confidence levels output by these models so that the stated probabilities match the actual likelihood of an event or classification being correct. This helps ensure that when a neural network says it…

Symbolic Knowledge Integration

Symbolic knowledge integration is the process of combining information from different sources using symbols, rules, or logic that computers can understand. It focuses on representing concepts and relationships in a structured way, making it easier for systems to reason and make decisions. This approach is often used to merge knowledge from databases, documents, or expert…

Knowledge-Driven Inference

Knowledge-driven inference is a method where computers or systems use existing knowledge, such as rules or facts, to draw conclusions or make decisions. Instead of relying only on patterns in data, these systems apply logic and structured information to infer new insights. This approach is common in expert systems, artificial intelligence, and data analysis where…