Pillar 2Governance Produces: AI Principles Statement
AI Principles
Turn borrowed values into institution-specific principles that actually decide cases.
Aligns withOECD AI PrinciplesNIST · GovernISO/IEC 42001
The idea
Principles are the values layer of the framework: the small set of commitments (fairness, transparency, accountability, human oversight, and privacy) that downstream decisions are intended to honor. Most institutions can state these commitments; the difficulty is that a principles statement adopted directly from an external source has limited operational effect. This pillar concerns principles that are local, grounded in the institution's mission, and operational, connected to actual decisions.
A principle such as transparency has limited effect until its specific requirements are defined, including what it requires when a unit deploys an undisclosed admissions chatbot. This pillar is therefore defined by concrete components: an adopted, institution-specific principles statement; decision norms that translate each principle into review questions used in procurement; a named forum that resolves tensions between principles; and sufficient visibility that those bound by the principles are aware of them.
Why it matters
Principles are where an institution's character meets its technology choices. They are the connective tissue between mission (Pillar 1) and the binding rules of policy (Pillar 3): without them policy is arbitrary, and with them policy has a rationale you can defend to a faculty senate. Done well, they let the institution say no to a tempting tool (and explain why) without re-litigating its values every time.
How you can tell
When it's working
The principles are specific to this institution, each with a one-line rationale tied to mission, not a generic copied list.
The principles were co-designed with faculty, students, and staff and ratified through shared governance, not issued by a single office.
Each principle maps to concrete review questions used in procurement and pilot decisions.
There is a named body and a known path for resolving conflicts between, say, innovation and equity.
A student, faculty member, or staffer can find the principles in under five minutes.
When it's missing
Principles exist as a webpage nobody consults; decisions never cite them.
Conflicts among innovation, access, academic freedom, and risk are settled by whoever is loudest.
Every contested case starts the values argument over from zero.
Most of the campus doesn't know the principles exist.
This is where the pillar stops being a principle and becomes work. Each practice below is something you can build, assign, and evidence, with a concrete first move, an owner, and what proof looks like. Move one practice up one level at a time.
1
Institutional AI principles statement
Institution-specific responsible-AI principles, aligned to mission and recognized standards.
1Almost no one outside the drafting group knows the principles exist.
3Principles are findable in under five minutes and included in onboarding.
5Awareness is measured (surveys, usage), and gaps trigger communication fixes.
Next moves
Apply the five-minute findability test from a student, faculty, and staff starting point.
Add the principles to new-employee and new-student onboarding.
Survey awareness annually and act on the result.
Who owns it
Communications · HR · Student affairs
Evidence it exists
Findability test results
Onboarding materials
Awareness survey data
In practice
Worked example
Transparency stops being a slogan
A community college's principles say 'transparency.' A department quietly launches an AI chatbot answering admissions questions; when challenged, staff note the principles 'don't mention chatbots.' Rather than argue, the AI governance body uses this pillar's machinery: it had already translated 'transparency' into three review questions, including 'are users told when they're interacting with AI?' Embedded in the procurement checklist. The chatbot is paused, a disclosure banner added, and the precedent logged so the next case is decided in minutes. The principle held because it had teeth.
Watch for
Adopting a long, aspirational list nobody can apply, five to eight principles with rationales beats fifteen slogans.
Leaving principles disconnected from procurement and the Compass, where the real decisions happen.
Never re-ratifying them, so they lose authority as leadership and context change.
Readiness & maturity questions
Readiness asks: are we prepared? Use these to surface blind spots before you build, honest “no” answers are where the work is. Representative prompts for reflection, not a scored test \u2014 for the scored version, use the Maturity Assessment.
Have institutional AI principles been created?
Are the principles inclusive of ethical concerns (e.g., bias, equity)?
Were diverse stakeholders involved in their development?
Are the principles publicly available?
Are faculty and staff trained on their application?
Do they apply to both academic and administrative AI?
Are they aligned with global standards (e.g., NIST, UNESCO)?
Is there a review cycle for the principles?
Are principles used in tool evaluation?
Are violations or misuses addressed via these principles?