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…
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…
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 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.
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…
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.
Fairness in AI refers to the effort to ensure artificial intelligence systems treat everyone equally and avoid discrimination. This means the technology should not favour certain groups or individuals over others based on factors like race, gender, age or background. Achieving fairness involves checking data, algorithms and outcomes to spot and fix any biases that…
A bias detection framework is a set of tools, methods, and processes designed to identify and measure biases in data, algorithms, or decision-making systems. Its goal is to help ensure that automated systems treat all individuals or groups fairly and do not inadvertently disadvantage anyone. These frameworks often include both quantitative checks, such as statistical…
Responsible AI governance is the set of rules, processes, and oversight that organisations use to ensure artificial intelligence systems are developed and used safely, ethically, and legally. It covers everything from setting clear policies and assigning responsibilities to monitoring AI performance and handling risks. The goal is to make sure AI benefits people without causing…
An AI Ethics Framework is a set of guidelines and principles designed to help people create and use artificial intelligence responsibly. It covers important topics such as fairness, transparency, privacy, and accountability to ensure that AI systems do not cause harm. Organisations use these frameworks to guide decisions about how AI is built and applied,…