Synthetic data generation is the process of creating artificial data that mimics real-world data. This data is produced by computer algorithms rather than being collected from actual events or people. It is often used when real data is unavailable, sensitive, or expensive to collect, allowing researchers and developers to test systems without risking privacy or…
Category: Artificial Intelligence
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…
Model Drift Detection
Model drift detection is the process of identifying when a machine learning model’s performance declines because the data it sees has changed over time. This can happen if the real-world conditions or patterns that the model was trained on are no longer the same. Detecting model drift helps ensure that predictions remain accurate and trustworthy…
AI Monitoring Framework
An AI monitoring framework is a set of tools, processes, and guidelines designed to track and assess the behaviour and performance of artificial intelligence systems. It helps organisations ensure their AI models work as intended, remain accurate over time, and comply with relevant standards or laws. These frameworks often include automated alerts, regular reporting, and…
Model Robustness Testing
Model robustness testing is the process of checking how well a machine learning model performs when faced with unexpected, noisy, or challenging data. The goal is to see if the model can still make accurate predictions even when the input data is slightly changed or contains errors. This helps ensure that the model works reliably…
Adversarial Defense Strategy
An adversarial defence strategy is a set of methods used to protect machine learning models from attacks that try to trick them with misleading or purposely altered data. These attacks, known as adversarial attacks, can cause models to make incorrect decisions, which can be risky in important applications like security or healthcare. The goal of…
AI Security Strategy
AI security strategy refers to the planning and measures taken to protect artificial intelligence systems from threats, misuse, or failures. This includes identifying risks, setting up safeguards, and monitoring AI behaviour to ensure it operates safely and as intended. A good AI security strategy helps organisations prevent data breaches, unauthorised use, and potential harm caused…
AI Audit Framework
An AI Audit Framework is a set of guidelines and processes used to review and assess artificial intelligence systems. It helps organisations check if their AI tools are working as intended, are fair, and follow relevant rules or ethics. By using this framework, companies can spot problems or risks in AI systems before they cause…
AI Compliance Strategy
An AI compliance strategy is a plan that helps organisations ensure their use of artificial intelligence follows laws, regulations, and ethical guidelines. It involves understanding what rules apply to their AI systems and putting processes in place to meet those requirements. This can include data protection, transparency, fairness, and regular monitoring to reduce risks and…
AI Risk Management
AI risk management is the process of identifying, assessing, and addressing potential problems that could arise when using artificial intelligence systems. It helps ensure that AI technologies are safe, fair, reliable, and do not cause unintended harm. This involves setting rules, monitoring systems, and making adjustments to reduce risks and improve outcomes.