Corporate Disruption in the Science of Machine Learning

TL;DR

This dissertation explores how increased corporate interest influences machine learning researchers' perspectives, practices, and career choices, highlighting the complex interplay between academia and industry within the field's cyclical history.

Contribution

It provides qualitative insights into researchers' experiences and perceptions regarding corporate influence, grounded in interviews and science and technology studies theory.

Findings

Researchers perceive increased corporate influence as shaping research agendas.

Academic and industry researchers experience shifts in collaboration and publication practices.

The study reveals nuanced views on the impact of corporate funding on research integrity.

Abstract

This MSc dissertation considers the effects of the current corporate interest on researchers in the field of machine learning. Situated within the field's cyclical history of academic, public and corporate interest, this dissertation investigates how current researchers view recent developments and negotiate their own research practices within an environment of increased commercial interest and funding. The original research consists of in-depth interviews with 12 machine learning researchers working in both academia and industry. Building on theory from science, technology and society studies, this dissertation problematizes the traditional narratives of the neoliberalization of academic research by allowing the researchers themselves to discuss how their career choices, working environments and interactions with others in the field have been affected by the reinvigorated corporate interest of recent years.