[Paper Review] Quantifying the Benefit of Artificial Intelligence for Scientific Research

Jian Gao, Dashun Wang|arXiv (Cornell University)|Apr 17, 2023

Artificial Intelligence in Healthcare and Education8 citations

TL;DR

The paper develops a measurement framework to quantify direct AI use and potential AI benefits in scientific research, applying NLP to 74.6 million publications and 7.1 million patents to reveal widespread AI use, citation premiums, and inequality implications.

ABSTRACT

The ongoing artificial intelligence (AI) revolution has the potential to change almost every line of work. As AI capabilities continue to improve in accuracy, robustness, and reach, AI may outperform and even replace human experts across many valuable tasks. Despite enormous effort devoted to understanding the impact of AI on labor and the economy and AI's recent successes in accelerating scientific discovery and progress, we lack a systematic understanding of how AI advances may benefit scientific research across disciplines and fields. Here, drawing from the literature on the future of work and the science of science, we develop a measurement framework to estimate both the direct use of AI and the potential benefit of AI in scientific research, applying natural language processing techniques to 74.6 million publications and 7.1 million patents. We find that the use of AI in research is widespread throughout the sciences, growing especially rapidly since 2015, and papers that use AI exhibit a citation premium, more likely to be highly cited both within and outside their disciplines. Moreover, our analysis reveals considerable potential for AI to benefit numerous scientific fields, yet a notable disconnect exists between AI education and its research applications, highlighting a mismatch between the supply of AI expertise and its demand in research. Lastly, we examine demographic disparities in AI's benefits across scientific disciplines and find that disciplines with a higher proportion of women or Black scientists tend to be associated with less benefit, suggesting that AI's growing impact on research may further exacerbate existing inequalities in science. As the connection between AI and scientific research deepens, our findings may become increasingly important, with implications for the equity and sustainability of the research enterprise.

Motivation & Objective

• Motivate a systematic understanding of how AI advances affect scientific research across disciplines.
• Develop a measurement framework to separate direct AI use from potential AI benefits in science.
• Apply natural language processing to large-scale publication and patent data to quantify AI presence and impacts.
• Assess demographic and educational mismatches between AI supply and research demand.
• Examine equity implications of AI benefits across scientific disciplines.

Proposed method

• Define a framework to distinguish direct AI use in research from the broader benefits AI may confer.
• Use natural language processing to analyze 74.6 million publications and 7.1 million patents for AI-related content.
• Quantify AI usage growth over time, particularly since 2015.
• Measure citation premium for AI-using papers (within and outside their disciplines).
• Assess disciplinary and demographic patterns in AI benefits (e.g., gender and race disparities).

Experimental results

Research questions

• RQ1 How widespread is the use of AI in scientific research across disciplines?
• RQ2 What is the citation impact (premium) of papers that use AI compared to non-AI papers?
• RQ3 What is the potential benefit of AI to different scientific fields, and where are gaps between AI education and research application?
• RQ4 How do AI benefits vary across demographic groups and disciplines, and what equity implications arise?

Key findings

• AI use in research is widespread across sciences and has grown rapidly since 2015.
• Papers that use AI exhibit a citation premium and are more likely to be highly cited within and outside their disciplines.
• There is substantial potential for AI to benefit many fields, but a mismatch exists between AI education supply and research demand.
• Disparities exist in AI benefits across disciplines, with higher proportions of women or Black scientists associated with less benefit.
• The expanding AI–science connection has implications for equity and sustainability in the research enterprise.