PreDefense: Defending Underserved AI Students and Researchers from Predatory Conferences

TL;DR

PreDefense is a mentorship program designed to help underserved AI students navigate legitimate conferences, avoiding predatory ones, and fostering integrity and diversity in AI research careers.

Contribution

The paper introduces PreDefense, a mentorship initiative aimed at guiding underrepresented students through conference selection and preparation to promote ethical research dissemination.

Findings

Enhanced awareness of predatory conferences among students.

Improved conference submission success rates for mentored students.

Increased diversity and integrity in AI research community.

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.