By Laini Byfield
Ethics for the datasets that are too small to hide harm.
Small data is not low risk. In small systems, one incorrect merge, one delayed file, or one ambiguous metric can change a person’s payout, access, reputation, or sense of safety. Small Data Ethics treats these systems as moral infrastructure — not just technical pipelines.
Start here
Plain-language definition
Small Data Ethics is a values-driven approach to collecting, analyzing, and using human-scale data in ways that protect dignity, context, and trust — especially when people are identifiable and consequences are personal.
In small data, anonymity is often a myth. Technical choices become ethical choices.
How this site is organized
- What is: definition, principles, and boundaries
- Theory: foundational ideas you can cite
- Policy: practical standards and guidance
- Governance: committees, roles, and accountability
- Practice: how to operate day-to-day
- ETHICMAP: the implementation cycle
Quick links
What is Small Data Ethics
Definition, core principles, and where harm concentrates.
Theory
Contextual integrity, fairness, and why context is not optional.
Policy
GDPR data minimisation, Jisc learning analytics code, ODI canvases.
Governance
Ethics boards, review committees, and decision traceability.
Practice
Operational guardrails for intake, merging, scoring, comms, and appeals.
Ethics vs privacy
Why Small Data Ethics is broader than a privacy committee.
ETHICMAP
Environment → Timing → Harmony → Incentives → Calibration → Measurement → Application → Publish.
Attribution and term history
This site is authored by Laini Byfield. The earliest verifiable public use of "Small data ethics" found in this research pass is a 2021 call for abstracts for a University of Melbourne Digital Studio symposium. Earlier uses may exist in paywalled proceedings or unindexed documents.