Applying Jevons' Paradox to AI, the paper argues that rebound effects mean efficiency gains may raise, not lower, AI's total environmental footprint.
The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Recent advances in deep learning methods for natural language processing (NLP) have created new business opportunities and made NLP research critical for industry development. As one of the big players in the field of NLP, together with governments and universities, it is important to track the influence of industry on research. In this study, we seek to quantify and characterize industry presence in the NLP community over time. Using a corpus with comprehensive metadata of 78,187 NLP publications and 701 resumes of NLP publication authors, we explore the industry presence in the field since the early 90s. We find that industry presence among NLP authors has been steady before a steep increase over the past five years (180% growth from 2017 to 2022). A few companies account for most of the publications and provide funding to academic researchers through grants and internships. Our study shows that the presence and impact of the industry on natural language processing research are significant and fast-growing. This work calls for increased transparency of industry influence in the field.
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From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate
Applying Jevons' Paradox to AI, the paper argues that rebound effects mean efficiency gains may raise, not lower, AI's total environmental footprint.