Category: Responsible AI

Robustness-Aware Training

Post author By EfficiencyAI
Post date 2 June 2025
Categories In Artificial Intelligence, Model Training & Tuning, Responsible AI

Robustness-aware training is a method in machine learning that focuses on making models less sensitive to small changes or errors in input data. By deliberately exposing models to slightly altered or adversarial examples during training, the models learn to make correct predictions even when faced with unexpected or noisy data. This approach helps ensure that…

Neural Network Robustness

Post author By EfficiencyAI
Post date 2 June 2025
Categories In Artificial Intelligence, Deep Learning, Responsible AI

Neural network robustness refers to how well a neural network can maintain its accuracy and performance even when faced with unexpected or challenging inputs, such as noisy data, small errors, or deliberate attacks. A robust neural network does not easily get confused or make mistakes when the data it processes is slightly different from what…

Risk Management Framework

Post author By EfficiencyAI
Post date 2 June 2025
Categories In Cybersecurity, Data Governance, Responsible AI

A Risk Management Framework is a structured process organisations use to identify, assess, and address potential risks that could impact their operations, projects, or goals. It provides clear steps for recognising risks, evaluating their likelihood and impact, and deciding how to minimise or manage them. By following a framework, organisations can make informed decisions, reduce…

Model Robustness Testing

Post author By EfficiencyAI
Post date 2 June 2025
Categories In Artificial Intelligence, Model Training & Tuning, Responsible AI

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…

AI Audit Framework

Post author By EfficiencyAI
Post date 2 June 2025
Categories In AI Governance, Artificial Intelligence, Responsible AI

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

Post author By EfficiencyAI
Post date 2 June 2025
Categories In AI Governance, Artificial Intelligence, Responsible AI

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

Post author By EfficiencyAI
Post date 2 June 2025
Categories In AI Governance, Artificial Intelligence, Responsible AI

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.

AI Accountability Framework

Post author By EfficiencyAI
Post date 2 June 2025
Categories In AI Governance, Artificial Intelligence, Responsible AI

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

Post author By EfficiencyAI
Post date 2 June 2025
Categories In Artificial Intelligence, Explainability & Interpretability, Responsible AI

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

Post author By EfficiencyAI
Post date 2 June 2025
Categories In AI Ethics & Bias, AI Governance, Responsible AI

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…