Responsible AI Principles

Responsible AI Principles

Instructor: AI CERTs Team

What you'll learn

Build Ethical and Accountable AI: Define fairness, transparency, accountability, and human oversight across the AI lifecycle.
Detect and Mitigate Bias: Evaluate datasets and models, apply mitigation techniques, and monitor equity outcomes.
Operationalize AI Governance: Create review processes, documentation, policies, and controls for responsible AI use.
Manage Risk, Privacy, and Security: Conduct AI risk assessments, address data protection, and plan for incidents and model drift.

Skills you'll gain

Tools you'll learn

Risking

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

8 assignments

Taught in English

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There are 9 modules in this course

AI now influences important decisions across business, government, healthcare, finance, and everyday digital experiences.

The Responsible AI Principles Certification is a practical, beginner-friendly course that helps professionals understand how to design, deploy, and manage AI systems that are ethical, transparent, fair, and accountable. This course focuses on real-world responsible AI workflows, including risk assessment, bias mitigation, privacy, transparency, documentation, monitoring, and incident response. You will explore key standards and practical methods for building safer AI across the full lifecycle, from data collection to deployment. Each module combines concise instruction with hands-on exercises, case studies, and policy templates. You will learn how to review AI systems, define governance processes, set measurable guardrails, and support safer decision-making. By the end, you will be able to assess AI risks, identify ethical concerns, apply responsible AI safeguards, and help build systems that protect users, reduce harm, and strengthen trust. Enroll online to build safe, fair, and accountable AI practices across your organization.

This module introduces the principles of AI ethics and the importance of developing and using AI responsibly. Learners explore key concepts such as fairness, transparency, accountability, privacy, and human-centered AI practices. The module establishes a foundation for understanding responsible AI development and adoption.

What's included

8 videos1 assignment1 plugin

8 videosTotal 32 minutes

Course Introduction4 minutes
1.1 Understanding AI in a Modern Ethics Context5 minutes
1.2 The Societal Impact of AI Technologies4 minutes
1.3 Core Principles and Stakeholders4 minutes
1.4 Building AI Literacy for the Workplace5 minutes
1.5 Human Rights, Democracy, and AI Ethics4 minutes
1.6 Case Study 1: Amazon’s AI Recruitment System and the Importance of Fair Hiring4 minutes
1.7 Case Study 2: Air Canada’s AI Chatbot and the Importance of AI Accountability3 minutes

1 assignmentTotal 30 minutes

Quiz 130 minutes

1 pluginTotal 5 minutes

Activity: Tab5 minutes

This module explores how bias can impact AI systems and influence outcomes across different applications. Learners understand methods for identifying, reducing, and managing bias while promoting fairness and inclusion. The module helps learners develop strategies for creating more equitable AI solutions.

What's included

6 videos1 assignment1 plugin

6 videosTotal 26 minutes

2.1 Where Bias Enters AI Systems5 minutes
2.2 Fairness Concepts and Practical Evaluation5 minutes
2.3 Mitigation and Inclusive Design5 minutes
2.4 Practical Fairness Scenarios5 minutes
2.5 Case Study 1: Workday and Algorithmic Hiring Fairness3 minutes
2.6 Case Study 2: Meta, Algorithmic Management, and AI-Driven Layoff Risk4 minutes

1 assignmentTotal 30 minutes

Quiz 230 minutes

1 pluginTotal 5 minutes

Activity: Hotspot5 minutes

This module explains the importance of making AI systems understandable, traceable, and well-documented. Learners explore explainable AI approaches, documentation practices, and techniques for improving trust in AI decisions. The module supports the development of transparent and accountable AI systems.

What's included

5 videos1 assignment1 plugin

5 videosTotal 22 minutes

3.1 Why Transparency Matters4 minutes
3.2 Explainability Methods and Documentation Standards5 minutes
3.3 Communicating AI Decisions Responsibly4 minutes
3.4 Transparency, Documentation, and Governance Practices4 minutes
3.5 Case Studies in Responsible AI Transparency5 minutes

1 assignmentTotal 30 minutes

Quiz 330 minutes

1 pluginTotal 5 minutes

Activity: Accordion5 minutes

This module focuses on protecting data and managing privacy risks throughout the AI lifecycle. Learners explore data governance practices, security measures, and responsible data handling approaches for AI systems. The module builds awareness of maintaining trust and compliance in AI-driven environments.

What's included

7 videos1 assignment1 plugin

7 videosTotal 33 minutes

4.1 AI Privacy Principles5 minutes
4.2 AI Data Governance and Data Quality5 minutes
4.3 Security Risks in AI Systems5 minutes
4.4 Privacy-Preserving AI Techniques5 minutes
4.5 Content Authenticity, Provenance, and Trust5 minutes
4.6 Case Study 1: OpenAI’s ChatGPT Enterprise and Enterprise Privacy Controls4 minutes
4.7 Case Study 2: Google Cloud’s Vertex AI and Enterprise AI Data Governance4 minutes

1 assignmentTotal 30 minutes

Quiz 430 minutes

1 pluginTotal 5 minutes

Activity: Drag and Drop5 minutes

This module introduces frameworks for establishing accountability and oversight in AI systems. Learners explore roles, responsibilities, governance structures, and monitoring practices that support responsible AI usage. The module helps organizations create effective approaches for managing AI risks and outcomes.

What's included

9 videos1 assignment1 plugin

9 videosTotal 34 minutes

5.1 Accountability Across the AI Lifecycle4 minutes
5.2 Human Oversight and Control5 minutes
5.3 AI Risk Management and Assurance4 minutes
5.4 Red Teaming and Safety Testing4 minutes
5.5 AI Governance Operating Model5 minutes
5.6 Grievance and Remedy Processes3 minutes
5.7 System Retirement and Decommissioning3 minutes
5.8 Case Study 1: Uber’s Automated Vehicle Testing and Meaningful Safety Oversight3 minutes
5.9 Case Study 2: New York City’s MyCity Chatbot and Evidence-Based Governance3 minutes

1 assignmentTotal 30 minutes

Quiz 530 minutes

1 pluginTotal 5 minutes

Activity: Flip Card5 minutes

This module examines the evolving legal, regulatory, and standards environment surrounding artificial intelligence. Learners explore key AI regulations, industry standards, and compliance considerations across different regions. The module helps learners understand how organizations can align AI practices with regulatory expectations.

What's included

7 videos1 assignment1 plugin

7 videosTotal 31 minutes

6.1 International Principles and Treaties5 minutes
6.2 AI Management and Technical Standards5 minutes
6.3 Binding Regional Laws5 minutes
6.4 National Guidance and Voluntary Frameworks4 minutes
6.5 Sector-Specific and Cross-Border Compliance5 minutes
6.6 Case Study 1: Rite Aid’s Facial Recognition System and Existing Consumer Protection Law3 minutes
6.7 Case Study 2: The Dutch SyRI System and Human Rights Protections3 minutes

1 assignmentTotal 30 minutes

Quiz 630 minutes

1 pluginTotal 5 minutes

Activity:True or Fasle5 minutes

This module explores responsible practices for deploying generative AI and agentic AI systems. Learners understand the opportunities, challenges, risks, and safeguards associated with advanced AI technologies. The module prepares learners to support secure, ethical, and effective AI adoption.

What's included

7 videos1 assignment1 plugin

7 videosTotal 32 minutes

7.1 How Modern Generative and Agentic AI Systems Work5 minutes
7.2 New Risks Introduced by Generative AI6 minutes
7.3 Agentic AI Risks and Governance5 minutes
7.4 Evaluation and Safe Deployment6 minutes
7.5 Responsible AI Use Cases and Boundaries4 minutes
7.6 Case Study 1: Mata v. Avianca and Fabricated Legal Citations3 minutes
7.7 Case Study 2: Deepfake Video Conference Fraud in Hong Kong3 minutes

1 assignmentTotal 30 minutes

Quiz 730 minutes

1 pluginTotal 5 minutes

Activity: Scenario5 minutes

This module provides a practical experience in applying AI ethics concepts through an impact assessment and governance plan. Learners evaluate AI use cases, identify risks, and design responsible AI strategies. The module enables learners to demonstrate their understanding of ethical AI implementation in real-world scenarios.