论文标题
道德考虑和行业参与机器学习研究的统计分析
Ethical Considerations and Statistical Analysis of Industry Involvement in Machine Learning Research
论文作者
论文摘要
行业参与机器学习（ML）社区似乎正在增加。但是，这种影响的定量规模和道德含义是相当未知的。为此，我们不仅对该领域进行了知情的道德分析，还检查了过去5年的主要ML会议神经，CVPR和ICML的所有论文，总共有近11,000篇论文。我们的统计方法着重于利益冲突，创新和性别平等。我们已经获得了四个主要发现：（1）学术界合作的数量正在增长。同时，我们发现很少披露利益冲突。 （2）行业发表了有关比学术界平均两年前趋势主题的论文。 （3）关于社会影响考虑因素，行业论文并没有落后于学术论文。 （4）最后，我们证明了工业论文在性别多样性之比的学术方面尚未达到其学术同行。我们认为，这项工作是ML社区内外的明智辩论的起点。
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.
