Responsible AI

The branch of applied ethics concerned with the moral implications of designing, deploying, and using artificial intelligence systems. AI ethics addresses issues including fairness, transparency, privacy, accountability, and the societal impact of automation.

The framework of policies, procedures, and organisational structures that guide the responsible development, deployment, and monitoring of artificial intelligence systems. AI governance encompasses risk management, ethical guidelines, regulatory compliance, model validation, and accountability mechanisms.

Systematic and repeatable errors in an AI system's outputs that create unfair outcomes for particular groups, typically arising from biased training data, flawed model design, or unrepresentative sampling. Algorithmic bias poses significant reputational, legal, and regulatory risks, and its identification and mitigation are core components of responsible AI governance.

A framework for evaluating a company's performance across environmental impact, social responsibility, and corporate governance practices. ESG factors are increasingly material to valuation, investor mandates, and regulatory compliance, and intersect with intangible asset categories such as reputation and organisational capital.

The documented lifecycle of data as it moves through an organisation's systems, showing its origin, transformations, dependencies, and destinations. Data lineage provides visibility into how data is created, processed, and consumed, enabling organisations to ensure data quality, comply with regulatory requirements (particularly GDPR's right to explanation), debug data pipeline issues, and assess the impact of system changes.

A branch of artificial intelligence concerned with enabling computers to understand, interpret, and generate human language. NLP powers applications such as chatbots, sentiment analysis, document classification, and automated contract review.

A set of practices combining machine learning, DevOps, and data engineering to standardise and streamline the end-to-end lifecycle of machine learning models, from development through deployment to monitoring. MLOps encompasses version control for models and data, automated testing, continuous integration and deployment, and model performance monitoring in production.

Artificial intelligence systems designed to provide human-interpretable explanations of their decision-making processes and outputs. Explainability is increasingly required by regulators — particularly in financial services, healthcare, and criminal justice — and is a key differentiator for AI products seeking enterprise adoption in regulated industries.