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.
Why It Matters
Model cards promote transparency and informed use. They help users understand what a model can and cannot do, preventing misuse and managing expectations.
Example
A model card for a facial recognition system stating: 'Tested accuracy: 99% for light skin tones, 87% for dark skin tones. Not recommended for law enforcement use.'
Think of it like...
Like a nutrition label on food — it gives you key facts about what is inside so you can make informed decisions about whether and how to use it.
Related Terms
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.
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.
Responsible AI
An approach to developing and deploying AI that prioritizes ethical considerations, fairness, transparency, accountability, and societal benefit throughout the entire AI lifecycle.
Bias in AI
Systematic errors in AI outputs that unfairly favor or disadvantage certain groups based on characteristics like race, gender, age, or socioeconomic status. Bias can originate from training data, model design, or deployment context.