Mainstream platforms’ content moderation systems typically employ generalized “one-size-fits-all” approaches, intended to serve both general and marginalized users. Thus, transgender people must often create their own technologies and moderation systems to meet their specific needs. In our interview study of transgender technology creators (n=115), we found that creators face issues of transphobic abuse and disproportionate content moderation. Trans tech creators address these issues by carefully moderating and vetting their userbases, centering trans contexts in content moderation systems, and employing collective governance and community models. Based on these findings, we argue that trans tech creators’ approaches to moderation offer important insights into how to better design for trans users, and ultimately, marginalized users in the larger platform ecology. We introduce the concept of trans-centered moderation – content moderation that reviews and successfully vets transphobic users, appoints trans moderators to effectively moderate trans contexts, considers the limitations and constraints of technology for addressing social challenges, and employs collective governance and community models. Trans-centered moderation can help to improve platform design for trans users while reducing the harm faced by trans people and marginalized users more broadly.
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This content will become publicly available on June 18, 2025
Learning to Persuade on the Fly: Robustness Against Ignorance
How Can Platforms Learn to Make Persuasive Recommendations?
Many online platforms make recommendations to users on content from creators or products from sellers. The motivation underlying such recommendations is to persuade users into taking actions that also serve system-wide goals. To do this effectively, a platform needs to know the underlying distribution of payoff-relevant variables (such as content or product quality). However, this distributional information is often lacking—for example, when new content creators or sellers join a platform. In “Learning to Persuade on the Fly: Robustness Against Ignorance,” Zu, Iyer, and Xu study how a platform can make persuasive recommendations over time in the absence of distributional knowledge using a learning-based approach. They first propose and motivate a robust-persuasiveness criterion for settings with incomplete information. They then design an efficient recommendation algorithm that satisfies this criterion and achieves low regret compared with the benchmark of complete distributional knowledge. Overall, by relaxing the strong assumption of complete distributional knowledge, this research extends the applicability of information design to more practical settings.
more » « less- Award ID(s):
- 2303372
- PAR ID:
- 10528612
- Publisher / Repository:
- Institute for Operations Research and the Management Sciences
- Date Published:
- Journal Name:
- Operations Research
- ISSN:
- 0030-364X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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