Ethical Considerations and Statistical Analysis of Industry Involvement in Machine Learning Research

TL;DR

This study analyzes industry involvement in ML research through ethical and statistical lenses, revealing increased collaborations, undisclosed conflicts of interest, earlier industry publications on trending topics, comparable social impact considerations, and lower gender diversity in industrial papers.

Contribution

It provides a comprehensive ethical and statistical analysis of industry influence in ML research, highlighting key trends and disparities over five years.

Findings

Growth in academic-corporate collaborations

Industry publishes trending topics earlier

Gender diversity is lower in industrial papers

Abstract

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.