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Navigating Algorithmic Bias: Why AI Bias Matters
Faculty: Dan Kipnis, STEM Librarian
Join a Rowan University Librarian for an interactive workshop exploring the hidden prejudices embedded within modern artificial intelligence and why they matter to everyone on our campus. Participants will dive deep into the AI development pipeline to identify five distinct categories of bias: historical, representation, cultural, algorithmic, and data drift. Through hands-on exercises, attendees will analyze real-world AI scenarios to detect these biases and understand the tangible harms they inflict on vulnerable communities. By critiquing the assumption that algorithms are inherently neutral or objective, we will equip you with the critical thinking skills needed to evaluate the technologies shaping our world. Whether you are a student, staff member, or faculty, this session will empower you to recognize and challenge algorithmic discrimination in both academic and everyday settings.
Learning Objectives:
- Identify five distinct categories of AI bias: historical, representation, cultural, algorithmic, and data drift, and distinguish how each originates in the AI development pipeline.
- Analyze a real-world AI output or scenario to detect which type(s) of bias may be present and articulate the potential harm to affected communities.
- Critique the assumption that AI systems are neutral or objective, using at least two hands-on examples from the workshop as evidence.
Skill levels for attendees: Little to no experience
Software requirements: None
- Date:
- Wednesday, September 30, 2026
- Time:
- 11:00am - 11:45am
- Presenter:
- Dan Kipnis, STEM Librarian
- Location:
- Online via Zoom
- Categories:
- Teaching, Learning, and Inquiry