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 o...

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Vydáno v:AI & society Ročník 38; číslo 1; s. 35 - 45
Hlavní autoři: Hagendorff, Thilo, Meding, Kristof
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.02.2023
Springer
Springer Nature B.V
Témata:
Artificial Intelligence
Computer Science
Conflicts of interest
Control
Engineering Economics
Ethics
Gender
Gender equality
Learning algorithms
Logistics
Machine learning
Marketing
Mechatronics
Methodology of the Social Sciences
Organization
Original Article
Performing Arts
Robotics
Social aspects
Statistical analysis
Statistics
Gender equality
Machine learning research
Industry influence
Conflict of interest
Public–private partnership
ISSN:0951-5666, 1435-5655
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Shrnutí: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.
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ISSN:0951-5666
1435-5655
DOI:10.1007/s00146-021-01284-z