PreDefense: Defending Underserved AI Students and Researchers from Predatory Conferences

Thomas Y. Chen
Proceedings of 2nd Workshop on Diversity in Artificial Intelligence (AIDBEI), PMLR 142:1-6, 2021.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v142-chen21a, title = {PreDefense: Defending Underserved AI Students and Researchers from Predatory Conferences}, author = {Chen, Thomas Y.}, booktitle = {Proceedings of 2nd Workshop on Diversity in Artificial Intelligence (AIDBEI)}, pages = {1--6}, year = {2021}, editor = {Lamba, Deepti and Hsu, William H.}, volume = {142}, series = {Proceedings of Machine Learning Research}, month = {09 Feb}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v142/chen21a/chen21a.pdf}, url = {https://proceedings.mlr.press/v142/chen21a.html}, 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.} }
Endnote
%0 Conference Paper %T PreDefense: Defending Underserved AI Students and Researchers from Predatory Conferences %A Thomas Y. Chen %B Proceedings of 2nd Workshop on Diversity in Artificial Intelligence (AIDBEI) %C Proceedings of Machine Learning Research %D 2021 %E Deepti Lamba %E William H. Hsu %F pmlr-v142-chen21a %I PMLR %P 1--6 %U https://proceedings.mlr.press/v142/chen21a.html %V 142 %X 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.
APA
Chen, T.Y.. (2021). PreDefense: Defending Underserved AI Students and Researchers from Predatory Conferences. Proceedings of 2nd Workshop on Diversity in Artificial Intelligence (AIDBEI), in Proceedings of Machine Learning Research 142:1-6 Available from https://proceedings.mlr.press/v142/chen21a.html.

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