Content Domain 4: Guidelines for Responsible AI
Domain 4 covers guidelines for responsible AI and represents 14% of the scored content on the exam.
Tasks
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 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 SageMaker Clarify, SageMaker Model Monitor, 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, SageMaker Model Cards, 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.
