Ethical considerations and statistical analysis of industry involvement in machine learning research

Industry involvement in the machine learning (ML) community seems to be increasing. However, the quantitative scale and ethical implications of this influence are rather unknown. For this purpose, we have not only carried out an informed ethical analysis of the field, but have inspected all papers of the main ML conferences NeurIPS, CVPR, and ICML of the last 5 years-almost 11,000 papers in total. Our statistical approach focuses on conflicts of interest, innovation, and gender equality. We have obtained four main findings. (1) Academic-corporate collaborations are growing in numbers. At the same time, we found that conflicts of interest are rarely disclosed. (2) Industry papers amply mention terms that relate to particular trending machine learning topics earlier than academia does. (3) Industry papers are not lagging behind academic papers with regard to how often they mention keywords that are proxies for social impact considerations. (4) Finally, we demonstrate that industry papers fall short of their academic counterparts with respect to the ratio of gender diversity. We believe that this work is a starting point for an informed debate within and outside of the ML community.

Automated summary

This study examines the industry involvement in the machine learning community by analyzing over 11,000 papers from the main ML conferences in the past five years. The findings reveal that academic-corporate collaborations are increasing, conflicts of interest lack disclosure, industry papers mention trending topics earlier than academia, social impact considerations are equally mentioned, but gender diversity falls short in industry papers.