Data Ethics
By External Provider
About this course
This course reframes AI ethics from a human-first, externality-first perspective rather than the GDP-and-shareholder framing that dominates industry discourse. Across four modules you trace how natural rights emerged from the French Revolution and post-WW2 charters, why those rights need a digital rewrite in the age of AI, how tech propaganda displaces critical thinking, and why the dominant economic models — including Shoshana Zuboff's surveillance capitalism — are broken. You leave with a working vocabulary for digital rights (privacy, consent, biometric data, freedom from algorithmic harm), a critical-thinking toolkit for resisting hype patterns like FOMO and naive utopianism, and concrete externality-first solutions such as taxing addictiveness ratios and crediting employee ownership. The course is opinionated — it does not pretend AI ethics is settled — and grounds every claim in named historical events, named books, and named patterns rather than abstract principle.
What you will learn
- Reframe AI ethics around human externalities — what is best for humans, not what is best for GDP or corporations
- Recognize and counter common tech-propaganda patterns: FOMO, naive utopianism, business-model circular logic, and Trojan-horse open-source plays
- Apply externality-first market corrections — addictiveness taxes, repairability credits, and humanism over GDP as a course-level KPI