PreDefense: Defending Underserved AI Students and Researchers from Predatory Conferences

EasyChair Preprint 4902

Abstract

Mentorship in the AI community is crucial to maintaining and increasing diversity, especially with respect to fostering the academic growth of underserved students. While the research process itself is important, there is not sufficient emphasis on the submission, presentation, and publication process, which is a cause for concern given the meteoric rise of predatory scientific conferences, which are based on profit only and have little to no peer review. These conferences are a direct threat to integrity in science by promoting work with little to no scientific merit. However, they also threaten diversity in the AI community by marginalizing underrepresented groups away from legitimate conferences due to convenience and targeting mechanisms like e-mail invitations. Due to the importance of conference presentation in AI research, this very specific problem must be addressed through direct mentorship. In this work, we propose PreDefense, a mentorship program that seeks to guide underrepresented students through the scientific conference and workshop process, with an emphasis on choosing legitimate venues that align with the specific work that the students are focused in and preparing students of all backgrounds for future successful, integrous AI research careers.

Keyphrases: Mentorship Program, beall list, bogus, conference, developing country, diversity, inclusion, mentorship, predatory, underserved student

@booklet{EasyChair:4902,
  author    = {Thomas Chen},
  title     = {PreDefense: Defending Underserved AI Students and Researchers from Predatory Conferences},
  howpublished = {EasyChair Preprint 4902},
  year      = {EasyChair, 2021}}