What’s Included?
High-Video 
AI Mentor 
Access for Tablet & Phone Prerequisites
- Basic understanding of Machine Learning (ML) concepts and the model lifecycle.
- Familiarity with foundational programming (e.g., Python) for toolkit usage.
- Subscription to the certificate program.
- Laptop or desktop computer with a stable internet connection.
- Basic literacy in data analysis and statistical concepts.
- No prior experience as an AI Ethicist or Policy Maker is required.
Skills You’ll Gain
- AI Bias Detection and Quantification
- Algorithmic Fairness Mitigation Techniques
- Model Explainability (XAI) Implementation
- Prompt Engineering for Ethical AI
- Ethical Risk Assessment using the Ethics Canvas
- Regulatory Compliance and Governance Frameworks (e.g., EU AI Act principles)
- Interpreting Academic Research (FAccT papers)
- Fairness-Performance Trade-off Analysis
- Auditing and Red-Teaming AI Systems
- Ethical Decision-Making in AI Design
Self Study Materials Included
Videos
Engaging visual content to enhance understanding and learning experience.
Tools You’ll Master
Hugging Face
AI Ethics Case Explorer (Markkula Center, SCU)
AI Fairness 360 (IBM)
Amazon SageMaker Clarify
Ethics Canvas
Explainable AI (XAI) by Google
FAccT Conference Papers (ACM)
Fairlearn (Microsoft)
MIT Moral Machine
What-If Tool (Google AI)
What You’ll Learn
Master the practical application of industry-leading bias mitigation toolkits (IBM, Microsoft, AWS).
Systematically identify, measure, and correct algorithmic bias in high-stakes contexts.
Gain proficiency in using Explainable AI (XAI) methods to demystify "black-box" models.
Apply structured ethical frameworks like the Ethics Canvas to project planning.
Conduct model audits that satisfy both internal governance and external regulatory demands.
Develop a critical understanding of data quality and its impact on fair outcomes.
Translate complex ethical dilemmas into actionable technical requirements for development teams.
Understand the key principles of global AI regulation, such as the EU AI Act.
Build technical documentation that clearly articulates a model's fairness and limitations.
Confidently lead discussions on the social and political impacts of AI deployment.
Frequently Asked Questions
It is ideal for Data Scientists, ML Engineers, AI Product Managers, Policy Analysts, and anyone responsible for building or governing trustworthy, production-level AI systems.
No. While basic ML knowledge is needed, the course introduces all necessary ethical frameworks, case law principles, and policy guidelines from the ground up.
Completion time varies by individual, but the guided curriculum is typically structured to take between 40 to 60 hours of focused study, including hands-on assignments.
The core toolkits, such as IBM AIF360, Fairlearn, and the What-If Tool, are free, open-source libraries. AWS SageMaker Clarify and similar tools may require access to a paid cloud service account.
Yes. This curriculum is based on globally recognized principles of Responsible AI, including those from NIST, OECD, and the principles underlying the EU AI Act, ensuring its relevance worldwide.
Absolutely. The course is built around using real-world case studies and industry toolkits to analyze common ethical failure points in domains like finance, hiring, and criminal justice.
It is a technical course that requires basic Python literacy to run the bias-mitigation toolkits. However, the focus is on applying the tools and interpreting the results, not advanced software engineering.
Both are core pillars. You will learn to measure and mitigate bias (Fairness) and implement techniques to understand and articulate model decisions (Explainability, or XAI).
