📚 Syllabus & Course Curriculum
Environmental Science & Sustainability
Module-by-module breakdown of Environmental & Social Impact of AI: Assessment, Metrics & Governance, from foundations to a certified capstone project.
Footprint
• Training against inference energy, and why inference dominates at scale
• Data centre PUE, water consumption for cooling and grid carbon intensity
• Published estimates and the reason most reported figures are not comparable
Measurement
• CodeCarbon and similar tooling for measuring a training run
• Scope 1, 2 and 3 emissions and where embodied hardware falls
• Market-based against location-based accounting and the offsetting critique
Social
• Algorithmic bias, exclusion and the distribution of error across groups
• Data labelling labour conditions in the supply chain
• Displacement, access inequality and concentration of compute
Disclosure
• Model cards, datasheets for datasets and system cards
• GRI, CSRD and ESG reporting frameworks as they begin to cover AI
• Distinguishing a substantive disclosure from greenwashing
Governance
• Impact assessment before deployment rather than after an incident
• Efficiency levers: model size, quantisation, scheduling and siting
• Procurement criteria and internal accountability that survives a deadline
Earn government-registered certification in Environmental & Social Impact of AI: Assessment, Metrics & Governance
e-Certificate and e-Marksheet issued on successful completion.
