Governance, Ethics & Equity Track
Self-paced track · 7 topics · novice → moderate
The judgment layer: how to verify AI output, protect data, weigh environmental cost, and keep AI use fair and accountable in public-health work. These pages deepen the ethics thread that runs through the core sessions and culminates in Core 4 · Verify, Disclose, Decide. Start from Core 1.
Difficulty levels
Novice getting started — no prior experience needed. Moderate comfortable with some AI use. Advanced experienced users going deep.
Topics
| # | Topic | Level | You’ll be able to… |
|---|---|---|---|
| 1 | Verifying AI Output | Novice | A repeatable workflow to catch hallucinations and bad citations |
| 2 | AI & Environmental Sustainability | Novice | The energy and water cost of AI, and how to right-size use |
| 3 | Data Security & Classification Deep-Dive | Novice | Match every tool to Drexel’s data classification |
| 4 | Responsible AI | Moderate | Accountability, transparency, and governance in practice |
| 5 | AI Bias & Health Equity Audit | Moderate | Test AI outputs across populations, not just on average |
| 6 | Keeping Your Judgment: Cognitive Offloading | Moderate | Use AI to amplify judgment, not replace it; know where to scrutinize |
| 7 | AI in Mentoring & Advising | Moderate | Where AI helps with advising tasks and where the relationship is paramount |
Not sure where to start?
Answer a few questions to find your starting point in this track.
Use public or non-sensitive data for every activity, and match any tool to Drexel’s data classification. See Tools & Access and Drexel’s AI Tools page. Study data is stricter: never in commercial/consumer AI tools, and even aggregate or de-identified study data needs the PI’s approval and must respect the IRB protocol, DUA, and the data’s terms of use — see the Research Data callout on Tools & Access.
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