An AI Governance RACI Matrix is a tool used to define roles and responsibilities for managing, developing, and overseeing artificial intelligence systems within an organisation. RACI stands for Responsible, Accountable, Consulted, and Informed, which are the four key roles assigned to tasks or decisions. By mapping out who does what in AI governance, organisations can…
Category: Responsible AI
Prompt Chain Transparency Logs
Prompt Chain Transparency Logs are records that track each step and change made during a sequence of prompts used in AI systems. These logs help users and developers understand how an AI model arrived at its final answer by showing the series of prompts and responses. This transparency supports accountability, troubleshooting, and improvement of prompt-based…
AI Usage Audit Checklists
AI Usage Audit Checklists are structured tools that help organisations review and monitor how artificial intelligence systems are being used. These checklists ensure that AI applications follow company policies, legal requirements, and ethical guidelines. They often include questions or criteria about data privacy, transparency, fairness, and security.
Agent Performance Review Loops
Agent Performance Review Loops are processes where the work or decisions made by an AI agent are regularly checked and evaluated. This feedback helps identify mistakes, improve outcomes, and guide the agent to learn from its experiences. The loop involves reviewing results, making adjustments, and then repeating the process to ensure ongoing improvement.
Conversation Failure Modes
Conversation failure modes are patterns or situations where communication between people breaks down or becomes ineffective. This can happen for many reasons, such as misunderstandings, talking past each other, or not listening properly. Recognising these failure modes helps people fix problems and improve their conversations. Understanding common ways conversations can go wrong lets teams or…
Hallucination Rate Tracking
Hallucination rate tracking is the process of monitoring how often an artificial intelligence system, especially a language model, generates incorrect or made-up information. By keeping track of these mistakes, developers and researchers can better understand where and why the AI makes errors. This helps them improve the system and ensure its outputs are more accurate…
AI Platform Governance Models
AI platform governance models are frameworks that set rules and processes for managing how artificial intelligence systems are developed, deployed, and maintained on a platform. These models help organisations decide who can access data, how decisions are made, and what safeguards are in place to ensure responsible use. Effective governance models can help prevent misuse,…
Human-in-the-Loop Governance
Human-in-the-loop governance refers to systems or decision-making processes where people remain actively involved, especially when technology or automation is used. It ensures that humans can oversee, review, and intervene in automated actions when needed. This approach helps maintain accountability, ethical standards, and adaptability in complex or sensitive situations.
Agent Accountability Mechanisms
Agent accountability mechanisms are systems and processes designed to ensure that agents, such as employees, artificial intelligence systems, or representatives, act responsibly and can be held answerable for their actions. These mechanisms help track decisions, clarify responsibilities, and provide ways to address any issues or mistakes. By putting these checks in place, organisations or individuals…
AI Copilot Evaluation Metrics
AI Copilot Evaluation Metrics are measurements used to assess how well an AI copilot, such as an assistant integrated into software, performs its tasks. These metrics help determine if the copilot is accurate, useful, and easy to interact with. They can include accuracy rates, user satisfaction scores, response times, and how often users rely on…