Principles That Hold
The six Microsoft Responsible AI principles, and how they translate into engineering behaviour.
The six, briefly
Microsoft's Responsible AI principles are:
- Fairness, treat people equitably.
- Reliability & Safety, perform as intended, safely.
- Privacy & Security, protect data and systems.
- Inclusiveness, work for the full range of human experience.
- Transparency, be understandable.
- Accountability, humans remain responsible.
They sound abstract. They aren't. Each one maps to engineering behaviour you already know how to do.
The engineering translation
| Principle | What it means in your repo |
|---|---|
| Fairness | Disaggregated evals across user segments. Bias tests in CI. |
| Reliability & Safety | Eval suite, red team, regression budget, kill switch. |
| Privacy & Security | Data minimization, secret scanning, threat model that names the LLM. |
| Inclusiveness | A11y baked in, multilingual evals, diverse test data. |
| Transparency | Model cards, disclosure copy, citations in UI, audit logs. |
| Accountability | A named owner, a runbook, a process for harm reports. |
A working test
For each principle, ask: "What would I show a regulator to demonstrate we take this seriously?"
If the honest answer is "a slide deck," you have a gap. If the answer is "a CI job, an owner, and a Friday review meeting," you're shipping responsibly.