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The Bullet Holes You Don't See: Survivorship Bias and What AI Leaves Out Online
This session uses stories such as statistician Abraham Wald and the “missing bullet holes” from World War II aircraft to help students, faculty, and staff think critically about AI tools and information literacy. AI tools can create a similar problem. Their outputs may look complete, but they can reflect gaps in training data, missing perspectives, underrepresented scholarship, and structural bias. Participants will explore how AI systems are trained in plain language, why certain voices or sources may be overrepresented or left out, and how this affects the information AI tools produce. The session will connect AI literacy with information literacy by asking participants to evaluate not only what an AI tool says, but also what may be missing, whose authority is being centered, and how users can verify information responsibly.
Learning Outcomes: By the end of the session, participants will be able to: Define AI literacy and information literacy in simple, practical terms; Describe how training data, model design, and source selection can shape AI outputs; Learn to identify possible missing voices, perspectives, or biases in AI-generated responses.
Presenters: Daniel Umana and Alla Webb
Faculty & Staff: Register for ILW events through Workday as well if you want professional development credits.
- Date:
- Wednesday, September 23, 2026
- Time:
- 11:00am - 12:00pm
- Time Zone:
- Eastern Time - US & Canada (change)
- Online:
- This is an online event. Event URL will be sent via registration email.
- Audience:
- Faculty & Staff Students
- Categories:
- Online Event