Building Ethical and Transparent AI Practices

In this course on Building Ethical and Transparent AI Practices, you’ll examine how to integrate core values like fairness, privacy, and accountability into your AI development and governance. You’ll also explore bias mitigation and oversight structures to reduce risk and support legal compliance. These practices help ensure that your AI systems meet public expectations and regulatory requirements.

You’ll explore ways to identify and reduce bias in model design and training. You’ll learn how to apply fairness audits, privacy-by-design principles, and transparency disclosures tailored to different users. You’ll also explore global data protection laws such as GDPR and CPRA, and outline clear roles and review processes that support strong oversight. By the end of this course, you’ll know how to design AI systems that reflect your organization’s ethical goals and regulatory obligations.

Learning Objectives:

• Define the core principles of ethical and trustworthy AI
• Identify sources of bias in data and model development
• Apply privacy-by-design and data governance practices
• Develop transparency strategies for high-risk and general-purpose systems
• Establish accountability roles and oversight structures