Applying the Decision Framework for AI Outputs

This set of vocabulary flashcards covers the decision-making framework for evaluating AI outputs, including key caveats, group activity roles, and the distinction between optimization and understanding.

Decision Framework (Model)

A four-step process involving identifying response design, potential risks, alternatives, and responsibility to make an informed decision on AI output use.

Failure patterns

Predictable ways in which AI outputs can be incorrect or misleading that users should practice recognizing.

Types of AI Failure Modes

Different categories of AI failure modes include output errors, misinterpretations, and ethical biases.

Ethical Bias in AI

Biases that stem from the training data or design of AI systems, which can affect decision-making.

Strategies to Mitigate AI Failure

Strategies include robust testing, continuous learning, and implementing human oversight.

Cultural Bias in AI

When AI systems reflect the cultural prejudices or limitations present in their training data, impacting their performance across diverse populations.