AI for Disaster Response refers to the use of artificial intelligence technologies to help manage and respond to natural or human-made disasters. These systems analyse large amounts of data quickly, helping emergency teams predict, detect, and respond to crises such as floods, earthquakes, or fires. By processing information from sensors, social media, and satellite images,…
Category: Responsible AI
AI for Predictive Healthcare
AI for Predictive Healthcare uses computer systems to analyse large amounts of health data and forecast potential medical outcomes. This technology helps doctors and healthcare professionals spot patterns in patient information that might signal future health problems. By predicting risks early, treatment can be given sooner, improving patient care and potentially saving lives.
Trustworthy AI Evaluation
Trustworthy AI evaluation is the process of checking whether artificial intelligence systems are safe, reliable and fair. It involves testing AI models to make sure they behave as expected, avoid harmful outcomes and respect user privacy. This means looking at how the AI makes decisions, whether it is biased, and if it can be trusted…
Neural Network Robustness Testing
Neural network robustness testing is the process of checking how well a neural network can handle unexpected or challenging inputs without making mistakes. This involves exposing the model to different types of data, including noisy, altered, or adversarial examples, to see if it still gives reliable results. The goal is to make sure the neural…
Dynamic Output Guardrails
Dynamic output guardrails are rules or boundaries set up in software systems, especially those using artificial intelligence, to control and adjust the kind of output produced based on changing situations or user inputs. Unlike static rules, these guardrails can change in real time, adapting to the context or requirements at hand. This helps ensure that…
Low-Confidence Output Handling
Low-Confidence Output Handling is a method used by computer systems and artificial intelligence to manage situations where their answers or decisions are uncertain. When a system is not sure about the result it has produced, it takes extra steps to ensure errors are minimised or users are informed. This may involve alerting a human, asking…
Compliance-Sensitive Output
Compliance-sensitive output refers to information or responses generated by a system that must follow specific legal, regulatory, or organisational requirements. These outputs are carefully managed to ensure they do not violate rules such as data privacy laws, industry standards, or internal policies. This concept is especially important for systems that process sensitive data or operate…
Prompt Ownership Framework
A Prompt Ownership Framework is a set of guidelines or rules that define who controls, manages, and has rights to prompts used with AI systems. It helps clarify who can edit, share, or benefit from the prompts, especially when they generate valuable content or outputs. This framework is important for organisations and individuals to avoid…
Operational Prompt Resilience
Operational Prompt Resilience refers to the ability of a system or process to maintain effective performance even when prompts are unclear, incomplete, or vary in structure. It ensures that an AI or automated tool can still produce useful and accurate results despite imperfect instructions. This concept is important for making AI tools more reliable and…
User Persona Contextualisation
User persona contextualisation is the process of adapting user personas to fit specific situations, environments, or use cases. It means understanding not just who the user is, but also the context in which they interact with a product or service. This approach helps teams design solutions that are more relevant and effective for real users…