AI Ethics in Action

SecAI+ Domain

4.0: AI Governance, Risk, and Compliance
2.0: Securing AI Systems

SecAI+ Objectives

4.2: Explain risks associated with AI.
4.3: Summarize the impact of compliance on business use and development of AI.
2.5: Given a scenario, implement monitoring and auditing for AI systems.

Overview

Ethics in Action is an interactive, scenario-based lab designed to build essential skills for responsible AI use in professional environments. In this lab, you’ll learn how to audit AI tool outputs for fairness, accuracy, and transparency, and develop practical strategies for handling digital ethics challenges. Through guided practice, you will examine real-world examples of AI bias, misinformation, and privacy risks, gaining confidence in making ethical decisions and recommending safe use policies for yourself or your organization.

Learning Objectives

  • Recognize signs of bias or stereotyping in AI-generated content
  • Detect and fact-check misinformation in AI outputs
  • Apply best practices to protect data privacy when using AI
  • Determine when human oversight is needed for AI-generated results

  • Draft guidelines for ethical and transparent use of AI in your workflow

     

Courses

Key terms and descriptions

AI Ethics
The study and practice of how artificial intelligence systems should be designed and used in morally responsible ways.
Bias
Systematic favoritism or unfairness in AI outputs caused by skewed data or algorithms.
Transparency
The principle of making AI processes, decisions, and data sources clear and accessible to users.
Fairness
The concept of ensuring that AI systems treat all individuals and groups equitably without discrimination.
Misinformation
False, misleading, or inaccurate content created or spread by AI models or users.
Oversight
Human supervision to review, verify, and approve AI-generated decisions or outputs to prevent errors and misuse.
Fact-Checking
The process of validating the truthfulness and accuracy of AI-created information using reliable sources.
Data Privacy
Protecting sensitive personal and organizational information from unauthorized access or exposure when using AI tools.
Stereotyping
Assigning generalized traits or characteristics to individuals or groups in AI outputs, often leading to unfair representation.
Guidelines
Written standards or recommendations that define acceptable and ethical practices for building and deploying AI systems in everyday workflows.