Transparency
The principle that AI systems should operate in a way that allows stakeholders to understand how they work, what data they use, and how decisions are made.
Why It Matters
Transparency builds trust and enables accountability. Opaque AI systems face increasing regulatory scrutiny and public resistance.
Example
A company publishing a transparency report detailing which AI models it uses, what data they were trained on, their known limitations, and how decisions are appealed.
Think of it like...
Like a restaurant with an open kitchen — customers can see exactly how their food is prepared, building trust through visibility.
Related Terms
Explainability
The ability to understand and articulate how an AI model reaches its decisions or predictions. Explainable AI (XAI) makes the decision-making process transparent and comprehensible to humans.
Interpretability
The degree to which a human can understand the internal mechanisms and reasoning process of a machine learning model. More interpretable models allow deeper inspection of how they work.
Model Card
A standardized document that accompanies a machine learning model, describing its intended use, performance metrics, limitations, training data, ethical considerations, and potential biases.
AI Governance
The frameworks, policies, processes, and organizational structures that guide the responsible development, deployment, and monitoring of AI systems within organizations and across society.
Responsible AI
An approach to developing and deploying AI that prioritizes ethical considerations, fairness, transparency, accountability, and societal benefit throughout the entire AI lifecycle.