CARE, ethics, and honest persuasion
10 November, 16:45-18:15.
FAIR asks whether data can be found and reused. CARE asks whether it should be — who it came from, who benefits, and who is harmed. We finish with persuasion: how evidence gets bent, and what to do when your own results surprise you. The slides give more detail, as well as references.
Key topics
- CARE principles
- Collective Benefit: who gains from sharing this data?
- Authority to Control: who decides how it can be used?
- Responsibility: are researchers accountable to those represented?
- Ethics: how can openness coexist with respect and consent?
- Case study: Archive of Our Own (AO3) — a community archive that is open, richly organised, and deliberately hard to scrape.
- Generative AI and training data — consent, credit, and the rule for this course.
- Ethical persuasive writing — how to argue fairly without manipulating evidence.
- How (not) to lie with statistics — cherry-picking, truncated axes, hidden uncertainty.
- When results surprise you — honest reporting, alternative explanations, and learning from unexpected findings.
- Writing styles — scientific, creative and encyclopedic, and how audience and purpose shape tone.
Why this matters
- CARE ensures openness does not harm people or communities.
- Sound governance upstream prevents misleading communication downstream.
And something lighter
