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…
Category: AI Ethics & Bias
AI Ethics Framework
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,…
Diversity Analytics
Diversity analytics refers to the use of data and analysis to measure and understand the range of differences within a group, such as a workplace or community. This includes tracking metrics related to gender, ethnicity, age, disability, and other characteristics. The goal is to provide clear insights that help organisations create fairer and more inclusive…
Customer Credit Risk Analytics
Customer credit risk analytics is the process of assessing how likely a customer is to repay borrowed money or meet credit obligations. It uses data and statistical methods to predict the chances that a customer will default on payments. This helps lenders and businesses make informed decisions about who to lend to and under what…
Inclusion Metrics in HR
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.
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…
Digital Ethics in Business
Digital ethics in business refers to the principles and standards that guide how companies use technology and digital information. It covers areas such as privacy, data protection, transparency, fairness, and responsible use of digital tools. The aim is to ensure that businesses treat customers, employees, and partners fairly when handling digital information. Companies following digital…
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…
Accessibility in Digital Systems
Accessibility in digital systems means designing websites, apps, and other digital tools so that everyone, including people with disabilities, can use them easily. This involves making sure that content is understandable, navigable, and usable by people who may use assistive technologies like screen readers or voice commands. Good accessibility helps remove barriers and ensures all…
Fairness-Aware Machine Learning
Fairness-Aware Machine Learning refers to developing and using machine learning models that aim to make decisions without favouring or discriminating against individuals or groups based on sensitive characteristics such as gender, race, or age. It involves identifying and reducing biases that can exist in data or algorithms to ensure fair outcomes for everyone affected by…