Inclusion metrics in HR are ways to measure how well a workplace supports people from different backgrounds, experiences and identities. These metrics help organisations understand if all employees feel welcome, respected and able to contribute. They can include survey results on belonging, representation data, participation rates in activities and feedback from staff.
Category: Responsible AI
Bias Mitigation in Business Data
Bias mitigation in business data refers to the methods and processes used to identify, reduce or remove unfair influences in data that can affect decision-making. This is important because biased data can lead to unfair outcomes, such as favouring one group over another or making inaccurate predictions. Businesses use various strategies like data cleaning, balancing…
Compliance Management
Compliance management is the process by which organisations ensure they follow laws, regulations, and internal policies relevant to their operations. It involves identifying requirements, setting up procedures to meet them, and monitoring activities to stay compliant. Effective compliance management helps reduce risks, avoid fines, and maintain a trustworthy reputation.
AI Model Interpretability
AI model interpretability is the ability to understand how and why an artificial intelligence model makes its decisions. It involves making the workings of complex models, like deep neural networks, more transparent and easier for humans to follow. This helps users trust and verify the results produced by AI systems.
Cognitive Bias Mitigation
Cognitive bias mitigation refers to strategies and techniques used to reduce the impact of automatic thinking errors that can influence decisions and judgements. These biases are mental shortcuts that can lead people to make choices that are not always logical or optimal. By recognising and addressing these biases, individuals and groups can make more accurate…
AI Explainability Frameworks
AI explainability frameworks are tools and methods designed to help people understand how artificial intelligence systems make decisions. These frameworks break down complex AI models so that their reasoning and outcomes can be examined and trusted. They are important for building confidence in AI, especially when the decisions affect people or require regulatory compliance.
Neural Network Robustness
Neural network robustness is the ability of a neural network to maintain accurate and reliable performance even when faced with unexpected or challenging inputs, such as noisy data or intentional attacks. Robustness helps ensure that the network does not make mistakes when small changes are made to the input. This is important for safety and…
AI-Driven Decision Systems
AI-driven decision systems are computer programmes that use artificial intelligence to help make choices or solve problems. They analyse data, spot patterns, and suggest or automate decisions that might otherwise need human judgement. These systems are used in areas like healthcare, finance, and logistics to support or speed up important decisions.
Incentive Alignment Mechanisms
Incentive alignment mechanisms are systems or rules designed to ensure that the interests of different people or groups working together are in harmony. They help make sure that everyone involved has a reason to work towards the same goal, reducing conflicts and encouraging cooperation. These mechanisms are often used in organisations, businesses, and collaborative projects…
Safe Reinforcement Learning
Safe Reinforcement Learning is a field of artificial intelligence that focuses on teaching machines to make decisions while avoiding actions that could cause harm or violate safety rules. It involves designing algorithms that not only aim to achieve goals but also respect limits and prevent unsafe outcomes. This approach is important when using AI in…