Overview
Transparency in AI systems is essential for building trust and ensuring that users understand how decisions are made. It involves explaining the processes behind AI outputs, which can help identify biases and improve fairness. As AI technologies become more integrated into various sectors, the need ...
Key Terms
Example: A model that provides clear reasons for its predictions is considered explainable.
Example: An AI hiring tool that treats all candidates equally regardless of gender.
Example: A company must address issues arising from biased AI decisions.
Example: A simple decision tree is more interpretable than a complex neural network.
Example: Deep learning models are often more complex than linear regression models.
Example: Users reporting issues with AI recommendations can lead to better model adjustments.