Using a training dataset that covers all relevant demographic groups exposes the model to varied examples and reduces the chance that it learns patterns that favor or exclude particular populations.
Reducing the sampling temperature only makes outputs more deterministic; it does not address underlying bias in the learned parameters.
Removing outliers that correspond to minority groups shrinks representation and tends to worsen bias.
Simply increasing the depth or parameter count of the network can even amplify existing bias if the data remain unbalanced.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What is a diverse and representative dataset in AI training?
Open an interactive chat with Bash
How does increasing the complexity of a model's architecture affect bias?
Open an interactive chat with Bash
Why is it important to evaluate a model before deployment?
Open an interactive chat with Bash
Microsoft Azure AI Fundamentals AI-900
Describe features of generative AI workloads on Azure
Your Score:
Report Issue
Bash, the Crucial Exams Chat Bot
AI Bot
Loading...
Loading...
Loading...
IT & Cybersecurity Package Join Premium for Full Access