Pomona's AI Use Policy

Last updated on 2026-08-26 | Edit this page

Overview

Questions

  • What is Pomona’s policy on AI tool usage?
  • What are the three data classification tiers?
  • What are the consequences of policy violations?

Objectives

  • Understand Pomona’s AI tool usage policy and guidelines
  • Know the three-tier data classification system
  • Recognize consequences of policy violations at each severity level

Pomona’s AI Tool Policy

Official title: “Use of AI Tools with Pomona College Data” — guidance on aligning AI usage with Pomona’s data governance structure and policies.

Where to find it: listed under Policies and Guidelines on the Data Governance page. The document itself is in Box and requires campus login.

Core principle: data classification determines which tools you can use.

Callout

This Episode Summarises; the Guideline Governs

What follows is a working summary for teaching. The Data Governance page is authoritative and is revised as tools and contracts change. Where this lesson and the current guideline disagree, the guideline wins — and please tell so the lesson gets fixed.

The same page also holds the Data Classifications document that defines the tiers used throughout this workshop.

Key Policy Principles

  1. Data Classification First: Classify data before using any AI tool
  2. Risk Proportionate: Higher risk data requires stricter controls
  3. Transparency Required: Disclose AI usage in research and coursework
  4. Compliance Non-Negotiable: Federal laws (FERPA, HIPAA) override convenience – no exceptions for “just this once”

Policy Quick Reference

PUBLIC data
  External AI: OK (ChatGPT, Claude, Gemini, etc.)
  Local AI: OK (Sagehen)

PROPRIETARY data
  External AI: NOT OK without written permission
  Local AI: Required

RESTRICTED data (FERPA, medical, export-control, PII)
  External AI: ABSOLUTELY NOT
  Local AI: Required + encryption + audit logs

Consequences of Violations

Minor (Good Faith Effort)

Scenario: Uploaded proprietary draft to ChatGPT (no FERPA/medical data). Result: Warning, informal discussion with advisor, required training.

Moderate (Negligence)

Scenario: Uploaded student work to ChatGPT without verifying anonymization. Result: Formal warning from Provost, required AI ethics training, possible publication retraction.

Severe (Intentional or Reckless)

Scenario: Uploaded student grades/names to ChatGPT knowing it was prohibited. Result: Disciplinary action up to termination, loss of research funding, possible legal prosecution, FERPA notification to affected students.

Who to Ask for Help

Contact For
Your Advisor Research context, data questions
Sagehen HPC, local AI, data classification
OVPR () Export control, IRB, compliance
Library Research Support Citation, attribution, data management
Provost’s Office Academic integrity, policy clarifications
Callout

When in Doubt, Ask First

Contact before using AI with any data you are unsure about. Getting help early prevents problems later. Compliance is a partnership between you, your advisor, the IRB (if applicable), and IT.

Challenge

Challenge: Policy Scenarios

For each scenario, what does Pomona’s policy require?

  1. You want to use ChatGPT to summarize a published paper
  2. You want to analyze unpublished lab results with Claude
  3. You want to use AI to detect patterns in student grade data
  1. Published paper (PUBLIC): External AI is fine. No restrictions.
  2. Unpublished lab results (PROPRIETARY): Local AI only (Sagehen), unless you obtain explicit written permission from all stakeholders.
  3. Student grade data (RESTRICTED/FERPA): Local AI only, with encryption and audit logging. Absolutely no external AI services.
Key Points
  • Pomona policy requires data classification before AI tool selection
  • PUBLIC data may use external AI; PROPRIETARY requires local AI or written permission; RESTRICTED data must never go to external AI
  • Violations can result in warnings, retractions, or legal consequences
  • When unsure, contact before proceeding