Content Domain 4: Guidelines for Responsible AI

Domain 4 covers guidelines for responsible AI and represents 14% of the scored content on the exam.

Topics

• Task Statement 4.1: Explain the development of AI systems that are responsible.
• Task Statement 4.2: Recognize the importance of transparent and explainable models.

Task Statement 4.1: Explain the development of AI systems that are responsible.

Objectives:

• Identify features of responsible AI (for example, bias, fairness, inclusivity, robustness, safety, veracity).
• Explain how to use tools to identify features of responsible AI (for example, Amazon Bedrock Guardrails).
• Define responsible practices to select a model (for example, environmental considerations, sustainability).
• Identify legal risks of working with generative AI (GenAI) (for example, intellectual property infringement claims, biased model outputs, loss of customer trust, end user risk, hallucinations).
• Identify characteristics of datasets (for example, inclusivity, diversity, curated data sources, balanced datasets).
• Describe effects of bias and variance (for example, effects on demographic groups, inaccuracy, overfitting, underfitting).
• Describe tools to detect and monitor bias, trustworthiness, and truthfulness (for example, analyzing label quality, human audits, subgroup analysis, Amazon Augmented AI [Amazon A2I]).

Task Statement 4.2: Recognize the importance of transparent and explainable models.

Objectives:

• Describe the differences between models that are transparent and explainable and models that are not transparent and explainable.
• Describe tools to identify transparent and explainable models (for example, Amazon SageMaker Model Cards, Amazon Bedrock Model Evaluations, open source models, data, licensing).
• Identify tradeoffs between model safety and transparency (for example, measure interpretability and performance).
• Describe principles of human-centered design for explainable AI (for example, user-feedback mechanisms, AI decision transparency).