📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI Fairness and Social Impact, from foundations to a certified capstone project.
Outline
Define fairness in AI and understand its importance • Analyze historical case studies of bias and harm in AI deployment • Learn key concepts: group fairness, individual fairness, procedural fairness
Outline
Learn popular fairness metrics and when to use them • Understand trade-offs between accuracy, fairness, and utility • Analyze technical vs. contextual fairness
Outline
Examine AI's impact on criminal justice, healthcare, and education • Analyze labor market impacts and discrimination in hiring tools • Discuss surveillance, policing, and AI at the margins
Outline
Learn about community engagement and participatory AI design • Analyze impact assessments and community consultations • Discuss building culturally responsive AI systems
Outline
Examine policy responses and legislative proposals for AI fairness • Learn about organizational governance: AI ethics boards and equity audits • Discuss transparency, documentation, and accountability mechanisms
Outline
Evaluate real-world AI systems for fairness and harm • Develop a capstone project: social impact assessment of an AI system • Learn to present findings to stakeholders
Earn government-registered certification in AI Fairness and Social Impact
e-Certificate and e-Marksheet issued on successful completion.
