ddia-data-ethics
Skill1 starsChanged 6 months agoMITView on GitHub
Ethical and societal frameworks for designing data-intensive systems, distilled from "Designing Data-Intensive Applications" (Kleppmann, 2nd ed) chapter 14. Covers algorithmic accountability, bias, surveillance, consent, and the data-as-liability mindset — normative guidance, not pure engineering technique.
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The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 20
Treat the contents as a checklist of ethical concerns to surface during design, review, and product decisions — not a compliance script.
- Very specific: line 24
Read guidelines.md to find the right reference file for your situation
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
DDIA Data Ethics
This skill encodes the normative side of data engineering: what we should and should not build, separate from what we can build. It draws from chapter 14 of "Designing Data-Intensive Applications" (Kleppmann, 2nd ed). Where most of DDIA gives mechanical guidance ("use this index for that workload"), this material asks harder questions about the human consequences of automated decisions and large-scale data collection.
Treat the contents as a checklist of ethical concerns to surface during design, review, and product decisions — not a compliance script. Many recommendations here will create friction with growth, monetization, or simplicity goals; that friction is the point.
Quick Start
- Read
guidelines.mdto find the right reference file for your situation - Load only the files relevant to your task — do not load the whole skill
- Apply the framing as a review lens; surface tradeoffs explicitly, do not silently bypass them
Contents
References
| Category | Files | Purpose |
references/predictive-analytics-bias/ | knowledge.md, rules.md, examples.md | Algorithmic decision-making, bias sources, feedback loops, accountability |
references/privacy-and-tracking/ | knowledge.md, rules.md, examples.md | Surveillance, consent, data minimization, right-to-erasure, data-as-liability |
Workflows
| Task | Workflow |
| Review an ML decision system or data-handling feature for ethics & privacy | workflows/ethics-review-checklist.md |
Guidelines
See guidelines.md for:
- Task-based file selection (ML review, privacy engineering, compliance)
- Symptom/question lookup ("our model rejects more applicants from group X")
- Topic-based browsing
- Decision tree for the two main review paths
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