Pacific McGeorge School of Law
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Contact Point Spring 2025
No full texthttps://scholarlycommons.pacific.edu/contact-point/1042/thumbnail.jp
ALIGN Panel: Patron Privacy v. Big Data
No full textThe titles of the panel presentations are: Speaking Out Against Data Brokerage in Academic Libraries (Michele Gibney, UOP and SPARC) Need vs. Interest: Telling the Difference When You\u27re Asked for Data (Jill Strykowski, SJSU)
When the Patriot Act enabled government to secretly surveil the borrowing and usage activities of library users, librarians adhered admirably to the ALA Bill of Rights tenet of safeguarding patrons\u27 “right to privacy and confidentiality in their library use” by ceasing to maintain borrowing records. Over twenty years hence, usage analytics and patron data are provided to libraries in abundant and granular detail by publishers and database vendors.
While such information provides valuable insight into user habits and preferences to improve services and demonstrate library value, as well as the ability to gauge student research habits to link to student-success initiatives, what has become of the concept of patron privacy? Are libraries at odds with the specificity of vendor-provided user data, or has privacy been sacrificed at the altar of greater insight as it is employed for more targeted collections, instruction, and advising decisions? Do users care about the data collected on them? How is user privacy addressed in resource licensing agreements? How are libraries responding when campus administrators ask for student data from the library? Are libraries alerting users to what data is collected from/about them when they use library services, and how that data is used? How are librarians grappling with these conflicting concepts?
Join ALIGN—CARL\u27s northern California interest group—as we host a panel and group discussion of these issues and what libraries are doing to help slow the erosion of our right to privacy.
Michele Gibney is the visiting program officer for Privacy & Surveillance at SPARC, focusing on raising awareness and developing strategies to address privacy threats in academic libraries. She is also the Head of Publishing and Scholarship Support at the University of the Pacific, where she manages the institutional repository and conducts additional scholarly communications work. She holds an MLIS from San Jose State University (2009), a Master\u27s in Asian Studies from the University of San Francisco (2006), and a B.A. in English from the University of Puget Sound. She is also currently pursuing a doctorate in Informatics from Linnaeus University in Sweden.
Jill Strykowski is the Cataloging and Government Documents Lead at San José State University.
She has a rich background in original cataloging work, archival digitization, library project management, library systems configuration and physical collection maintenance. Her current scholarly interests focus on how library science is impacted by AI tools, linked data, and the data brokering economy.
Ms. Strykowski has a master\u27s degree in Library & Information Science from Long Island University, and another in Archives & Public History from New York University. And she is excited to start her PhD work this fall through the SJSU iSchool\u27s Gateway PhD partnership with Manchester Metropolitan University
How Euler Could Have Done It: Euler and a direct proof for the functional equation for the Riemann zeta-function
No full textWe present a chain of argumentation for a direct proof of the functional equation for the Riemann ζ–function that Euler could have presented
Artificial Intelligence Approaches to 3D Facial Imaging Assessment
No full textObjectives: The primary aim of this study was to evaluate the accuracy and precision of a deep learning artificial intelligence (AI) model in reconstructing 3D facial geometry from a single 2D photograph. The study specifically measured the coordinate differences of human-placed facial landmarks on the AI-generated models against those placed on 3D models from a gold-standard scanner (Bellus3D). Secondary objectives included identifying dimensional variations (X, Y, Z) and systematic biases in the AI reconstructions. Methods: This was a retrospective comparative study using a sample of 47 anonymized patient records from the University of the Pacific database. For each patient, a 3D facial model was generated from a 2D photograph using the 3DDFA-V2 deep learning model. These AI-generated models were compared to reference models captured on the same day using a Bellus3D scanner. Two trained observers identified a set of facial landmarks on both the AI-generated and Bellus3D models. The coordinate data were normalized using Procrustes analysis to compare shape and landmark position independent of scale and orientation. The differences in landmark coordinates between the two model types were then statistically analyzed. Results: The analysis revealed that human landmark identification on the AI-generated models showed consistently lower variability compared to the gold-standard scans, suggesting that the smoother surfaces of the photo-reconstructed models may lead to more repeatable measurements. While the AI model demonstrated high fidelity, systematic errors were observed; for instance, a tendency to place certain landmarks (e.g., Me, Pog) in a more inferior position (negative Y-axis error). The greatest overall deviations (Euclidean error) were found in landmarks associated with the lateral and inferior borders of the face, such as Right Gonion (RGo), Trichion (Tr), and Left Gonion (LGo). Conclusions: The AI deep learning model is a promising low-cost tool for generating 3D facial models from 2D photographs with a high degree of precision in landmark placement. While the AI models facilitated more repeatable landmarking, they also exhibited some systematic biases. These findings have clinical implications for the use of photo-reconstructed models in diagnosis and treatment planning, highlighting areas where further refinement of the AI algorithm is needed
