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

arXiv: https://arxiv.org/abs/2006.04541 · HTML: https://mlsanity.com/paper/2006.04541/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [Institutionalising Ethics in AI through Broader Impact Requirements](https://mlsanity.com/paper/2106.11039/) (2021)
- [AI Ethics Statements -- Analysis and lessons learnt from NeurIPS Broader Impact Statements](https://mlsanity.com/paper/2111.01705/) (2021)
- [Analyzing the Impact of Companies on AI Research Based on Publications](https://mlsanity.com/paper/2310.20444/) (2023)
- [Big Tech influence over AI research revisited: memetic analysis of attribution of ideas to affiliation](https://mlsanity.com/paper/2312.12881/) (2023)
- [Social and environmental impact of recent developments in machine learning on biology and chemistry research](https://mlsanity.com/paper/2210.00356/) (2022)
- [From Bias to Balance: Fairness-Aware Paper Recommendation for Equitable Peer Review](https://mlsanity.com/paper/2602.22438/) (2026)

## Abstract (arXiv)

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.