Ethical Technology Use
Goal
Make responsible choices that protect people, reduce risk, and preserve trust.
Trigger signals
- •“Can I put this data in an AI tool?” “Is this ok to scrape/log/store?”
- •Shipping features that affect users (ranking, recommendations, surveillance-like analytics).
- •Generating content/code with AI where IP, privacy, or attribution matters.
Core workflow (fast checklist)
- •Data classification: public / internal / personal / sensitive.
- •Consent & purpose: Do we have consent? Is the use aligned with the purpose?
- •Minimization: Can we remove or redact identifiers?
- •Security: Where is it stored? Who can access? Retention period?
- •Fairness: Who could be harmed? Any bias or exclusion risks?
- •Transparency: What should we disclose (to users, students, stakeholders)?
- •Accountability: Who owns the decision and review?
Practical defaults
- •Never share sensitive personal data with third-party tools without explicit permission and safeguards.
- •Prefer “least data, least time”: minimize fields and keep retention short.
- •Document decisions: one paragraph decision record with risks + mitigations.
Templates
Decision record (one paragraph)
“We will use [tool] for [purpose] with [data type]. Risks: [privacy/bias/security]. Mitigations: [redaction, access control, retention]. Owner: [name]. Review date: [date].”
Transparency line (user-facing)
“We use automated tools to support [task]. You can opt out / request review via [path].”
Output defaults
- •Provide a go/no-go recommendation with mitigations.
- •Provide a short policy-style snippet the user can paste into docs.
Guardrails
If legal/compliance is uncertain, recommend consultation with DPO/legal/security and keep advice conservative.