An AI Accountability Framework is a set of guidelines, processes and tools designed to ensure that artificial intelligence systems are developed and used responsibly. It helps organisations track who is responsible for decisions made by AI, and makes sure that these systems are fair, transparent and safe. By following such a framework, companies and governments…
Category: Artificial Intelligence
AI Transparency
AI transparency means making it clear how artificial intelligence systems make decisions and what data they use. This helps people understand and trust how these systems work. Transparency can include sharing information about the algorithms, training data, and the reasons behind specific decisions.
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.
Model Interpretability Framework
A Model Interpretability Framework is a set of tools and methods that help people understand how machine learning models make decisions. It provides ways to explain which features or data points most affect the model’s predictions, making complex models easier to understand. This helps users build trust in the model, check for errors, and ensure…
Model Scalability Strategy
A model scalability strategy is a plan for how to grow or adapt a machine learning model to handle larger amounts of data, more users, or increased complexity. This involves choosing methods and tools that let the model work efficiently as demands increase. Without a good scalability strategy, a model might become too slow, inaccurate,…
Inference Optimization
Inference optimisation refers to making machine learning models run faster and more efficiently when they are used to make predictions. It involves adjusting the way a model processes data so that it can deliver results quickly, often with less computing power. This is important for applications where speed and resource use matter, such as mobile…
AI Model Deployment
AI model deployment is the process of making an artificial intelligence model available for use after it has been trained. This involves setting up the model so that it can receive input data, make predictions, and provide results to users or other software systems. Deployment ensures the model works efficiently and reliably in a real-world…
Model Retraining Strategy
A model retraining strategy is a planned approach for updating a machine learning model with new data over time. As more information becomes available or as patterns change, retraining helps keep the model accurate and relevant. The strategy outlines how often to retrain, what data to use, and how to evaluate the improved model before…
Model Monitoring Framework
A model monitoring framework is a set of tools and processes used to track the performance and health of machine learning models after they have been deployed. It helps detect issues such as data drift, model errors, and unexpected changes in predictions, ensuring the model continues to function as expected over time. Regular monitoring allows…
Model Lifecycle Management
Model lifecycle management is the process of overseeing the development, deployment, monitoring, and retirement of machine learning models. It ensures that models are built, tested, deployed, and maintained in a structured way. This approach helps organisations keep their models accurate, reliable, and up-to-date as data or requirements change.