# Content Domain 4: Guidelines for Responsible AI
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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.](#ai-practitioner-01-task4.1)
+ [Task Statement 4.2: Recognize the importance of transparent and explainable models.](#ai-practitioner-01-task4.2)

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