AI Transparency
The principle that AI systems should be open and clear about their operations, decisions, and data usage.
Definition
The proactive publication and communication of AI system details—data provenance, model architectures, training parameters, performance metrics, and known limitations—to build stakeholder trust and facilitate oversight.
Real-World Example
A public health agency publishes an interactive dashboard detailing its COVID-19 outbreak-prediction model: data sources, training dates, algorithm version, error margins, and key assumptions. Researchers and policymakers can download the code and data, fostering collaborative improvement.
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