  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 publishes
papers about trending ML topics on average two years earlier than academia
does. (3) Industry papers are not lagging behind academic papers in regard to
social impact considerations. (4) Finally, we demonstrate that industrial
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.
