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Compliance, transparency, bias, and risk
A model card is a short document that explains how a machine learning model works, what it was built for, and what its limitations are. Think of it as a nutrition label for AI. It tells you what went into…
A Model Card is a transparent documentation report that describes the performance, limitations, and intended use of an artificial intelligence or machine learning model. It helps stakeholders understand how a model was built, what data it was trained on, and…
Model interpretability is the ability to understand how and why an artificial intelligence or machine learning model makes its predictions. It provides transparency into a model’s decision-making process — revealing which data features influenced the outcome and how they interacted…
Explainability in AI refers to the ability to understand and interpret how an artificial intelligence or machine learning model arrives at its predictions or decisions. It answers the question: “Why did the AI make that choice?” As AI becomes more…
Hallucination prevention in AI refers to the methods and techniques used to reduce false or fabricated outputs generated by artificial intelligence systems. In the context of large language models and generative AI, a hallucination occurs when an AI confidently produces…
Human-in-the-Loop (HITL) is an approach in artificial intelligence where humans actively participate in the training, validation, or operation of a model to improve its accuracy and reliability. Rather than fully automating decision-making, HITL systems combine the speed of machines with…
Compliance automation is the use of technology to streamline, monitor, and enforce regulatory and policy requirements with minimal manual effort. In data and Artificial Intelligence (AI) environments, it helps organisations stay aligned with laws, standards, and internal policies while reducing…
Data governance is the framework of policies, processes, and responsibilities that ensures an organisation’s data is accurate, consistent, secure, and used ethically. It defines how data is collected, stored, managed, and shared to maintain quality, compliance, and accountability across the…
Data lineage is the process of tracking the life cycle of data, where it originates, how it moves, transforms, and where it is ultimately used. It provides a transparent view of data flow across systems, helping organisations understand dependencies, ensure…
Data privacy refers to the responsible handling, processing, and protection of personal and sensitive information. It ensures that individuals have control over how their data is collected, used, and shared, while organisations stay compliant with legal frameworks such as GDPR…