This panel explores the evolving relationship between commercial platforms, generative AI, and digital public knowledge infrastructures. As community-led initiatives like Wikimedia operate alongside increasingly closed and opaque commercial systems, the need for greater transparency, data access, and research tools becomes critical. Generative AI introduces new dynamics—shaping how knowledge is accessed, interpreted, and produced—while also raising ethical concerns around automation, transparency, and positionality in research.

Additionally, the use of sensitive data, such as biometrics, in AI training prompts important questions about how classification systems are formed and how they impact representation and fairness. The panel addresses these challenges and emphasizes the importance of responsible, ethical approaches to studying and shaping AI-driven knowledge ecosystems.

Opening Remarks
Karine Morin (President, CFHSS)

Panel Chair
Ray Siemens (U Victoria) 

“Observations on Wikimedia, LLMs and Information Ecosystem Observability”
Amanda Lawrence (RMIT U)

“Forging Interpretations with Generative AI”
Geoffrey Rockwell (U Alberta, Amii)

"Ethical Data Collection for AI: Bridging Knowledge Platforms and Biometric Datasets"
Lai-Tze Fan (U Waterloo)

1. artificial intelligence
2. large language models