Overview
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Learn about the practices needed to perform fairness testing and implement responsible AI systems.
Syllabus
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Introduction
- Understanding responsible AI
- What is AI and how does data enable it?
- Modern AI development
- Problems in ML that differ from software engineering
- Big data and where it comes from
- Seeing trends in data
- Building data understanding
- Visualization and comparing data
- Storytelling with data
- Introduction to ethical AI
- Ethical frameworks
- Beneficence vs. maleficence
- Calculating consequences
- Consequence scanning
- Common good and equity
- Fairness
- Transparency
- Accountability
- Explanations
- Interpretability
- Inclusivity
- Why fairness related harms?
- Critical AI incidents and learnings
- Bias in the design and development lifecycle
- Causal reasoning and fairness
- Risk mitigation in AI
- Technical aspects of sociotechnical solutions
- Anonymity and data privacy
- Unintended uses and misuses
- Unethical business cases
- Autonomous systems and society
- Who AI is developed for?
- AI regulation and applying responsible AI frameworks
