AI Ethics
Definition
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. Organisations with robust AI ethics frameworks are better positioned to manage regulatory risk and maintain stakeholder trust.
Complementary Terms
Concepts that frequently appear alongside AI Ethics in practice.
A framework for developing and deploying artificial intelligence systems that are fair, transparent, accountable, and aligned with human values and societal well-being. Responsible AI encompasses technical practices such as bias testing and model interpretability, alongside governance processes including ethical review boards, impact assessments, and stakeholder engagement.
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.
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.
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 field of artificial intelligence that enables machines to interpret and extract information from visual inputs such as images, video, and documents. Computer vision is applied in quality inspection, medical imaging, autonomous vehicles, and document processing.
A structured process required under GDPR Article 35 to identify, assess, and mitigate privacy risks arising from data processing activities that are likely to result in high risk to individuals. DPIAs are mandatory before deploying new technologies, large-scale profiling, or processing sensitive personal data, and must document the necessity, proportionality, and safeguards of the proposed processing.
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.
A category of artificial intelligence systems capable of creating new content — including text, images, code, music, and video — based on patterns learned from training data. Generative AI is transforming content production, product design, and software development, raising novel questions about intellectual property ownership and the valuation of AI-generated outputs.
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