Qian Ma

Qian Ma, Haitao Mao, Jingzhe Liu et al.

Self-supervised learning~(SSL) is essential to obtain foundation models in NLP and CV domains via effectively leveraging knowledge in large-scale unlabeled data. The reason for its success is that a suitable SSL design can help the model to follow the neural scaling law, i.e., the performance consistently improves with increasing model and dataset sizes. However, it remains a mystery whether existing SSL in the graph domain can follow the scaling behavior toward building Graph Foundation Models~(GFMs) with large-scale pre-training. In this study, we examine whether existing graph SSL techniques can follow the neural scaling behavior with the potential to serve as the essential component for GFMs. Our benchmark includes comprehensive SSL technique implementations with analysis conducted on both the conventional SSL setting and many new settings adopted in other domains. Surprisingly, despite the SSL loss continuously decreasing, no existing graph SSL techniques follow the neural scaling behavior on the downstream performance. The model performance only merely fluctuates on different data scales and model scales. Instead of the scales, the key factors influencing the performance are the choices of model architecture and pretext task design. This paper examines existing SSL techniques for the feasibility of Graph SSL techniques in developing GFMs and opens a new direction for graph SSL design with the new evaluation prototype. Our code implementation is available online to ease reproducibility on https://github.com/GraphSSLScaling/GraphSSLScaling.

Qian Ma, Aditya Majumdar, Sarah Rajtmajer et al.

Privacy policies govern how personal data is collected, used, and shared. Yet, in most privacy-policy consent flows, agreement is operationalized as a single click at the end of a long, opaque policy document. Recent privacy-law scholarship has argued for a standard of demonstrably informed consent. That is, the party drafting and designing privacy-policy consent mechanisms must generate reliable evidence that a person demonstrates comprehension of the consequential terms to which they agree. To this end, we study pedagogical friction as a design framing: minimal interventions embedded within a privacy-policy consent flow that aim to support demonstrated comprehension while keeping burden on the user low. In a randomized experiment, we tested pedagogical friction for demonstrably informed consent in the context of a privacy policy for an edtech app for young children. We recruited 293 parents of kids ages 3-8 to review the app's privacy policy under one of six conditions that varied presentation format and pacing, then complete a six-question comprehension quiz. Three conditions offered a second policy review and quiz retake for participants who did not pass this quiz on their first attempt. We find that the slide-based condition (G3) achieved the highest first-attempt threshold attainment (>=80%) (41.7%), followed by the paced, sectioned condition (G4) (30.6%). In the retake conditions, 64.9% of participants who completed a second attempt improved their score. Notably, in conditions that did not gate consent on demonstrated comprehension, 97.3% of participants who scored below the threshold still chose to consent, suggesting that ungated consent flows can record agreement without demonstrated comprehension. Our results suggest that pedagogical friction can strengthen the evidentiary basis of consent and clarify what it costs in time and burden.