Future of work: Ethical foundations
Apply Future of work through stakeholders, harms, rights.
Beginner / Structured learning draft / Version 0.9
Structured learning draftAI's Impact on Work & Society
Evaluate technology decisions with evidence and accountability. You will finish with a documented risk and impact assessment.
This open learning manuscript is undergoing course-specific editorial review. It does not provide certification. Progress and notes are stored only in this browser.
Responsible AITool stack
OpenAI
12guided lessons
4focused modules
1reviewable project
PDFoffline workbook
The problem
What this course helps you do
AI's Impact on Work & Society is a structured, practical course covering Future of work, Automation, Inequality, AI society. It emphasizes explainable methods, checked work and a final outcome that can be reviewed.
Who it is for
Product, policy and technical learners evaluating responsible AI.
Prerequisites
- No specialist background required
Portfolio-ready project
Produce a reviewable AI's Impact on Work & Society project using Future of work, Automation, Inequality.
Deliverable: A working AI's Impact on Work & Society artefact plus an evidence-based self-review.
Practice fileTechnology impact reviewA risk, affected-party, evidence and mitigation worksheet.Planning template · MarkdownProject briefDefine the user, outcome, boundaries, evidence and acceptance criteria before building.Printable workbook · PDFComplete course workbookA4-ready lesson prompts, project worksheets, evidence logs and references.
Downloads open without an account. Practice data is fictional; review each file before using it in another system.
Definition of done
- The output matches the stated outcome.
- Inputs and decisions are reproducible.
- Boundary and failure evidence is included.
- Limitations and responsibility considerations are explicit.
Course plan
From first concept to finished work
Automation: Evidence
Apply Automation through bias, measurement, impact.
Inequality: Governance
Apply Inequality through accountability, documentation, oversight.
AI society: Practice
Apply AI society through risk review, participation, monitoring.
Source-led learning
Authoritative references
Course claims are grounded in primary documentation and recognized public guidance. Recheck version-sensitive information before professional use.