Evaluating AI output is crucial when using any AI tool to ensure accuracy, reliability, and ethical integrity. This helps to avoid the dissemination of misinformation, ensures the AI’s decisions and suggestions align with human values and standards, and confirms that the tool functions as intended without unintended biases or errors.
To ensure accuracy and reliability:
Verify the information by cross-referencing with credible sources. Look for citations and references to confirm the content is supported by reliable data.
Make sure the output directly addresses your query or topic of interest, and check for clarity and logical presentation.
Be vigilant for any inconsistencies or contradictions within the output.
We must understand the context of AI outputs and uphold high standards for data quality. AI should support—not replace institutional logic.
Actively identify and mitigate algorithmic bias. Our use of AI should support diversity and ensure no group is unfairly disadvantaged by automated patterns.
Ensure the final product contains your unique "personal touch" and institutional expertise.
While we can co-create with algorithms, humans are the sole decision makers.
Support for citing and evaluating AI output can be found on the Lavery Library LibGuide: AI Tools & Resources.