The Narrow Depth and Breadth of Corporate Responsible AI Research

TL;DR

This study analyzes over 6 million articles and 32 million patent citations to reveal that industry engagement in responsible AI research is limited and less diverse compared to conventional AI research, raising concerns about societal alignment.

Contribution

The paper provides large-scale, empirical evidence of the gap between industry and academia in responsible AI research and its limited influence on commercialization practices.

Findings

Industry's responsible AI research is substantially lower than conventional AI research.

Responsible AI research within industry covers fewer key topics.

Limited linkage exists between responsible AI research and AI patent commercialization.

Abstract

The transformative potential of AI presents remarkable opportunities, but also significant risks, underscoring the importance of responsible AI development and deployment. Despite a growing emphasis on this area, there is limited understanding of industry's engagement in responsible AI research, i.e., the systematic examination of AI's ethical, social, and legal dimensions. To address this gap, we analyzed over 6 million peer-reviewed articles and 32 million patent citations using multiple methods across five distinct datasets to quantify industry's engagement. Our analysis reveals notable heterogeneity between industry's substantial presence in conventional AI research and its comparatively modest engagement in responsible AI. Leading AI firms exhibit significantly lower output in responsible AI research compared to their conventional AI research and the contributions of leading academic institutions. Our linguistic analysis reveals a more concentrated scope of responsible AI research within industry, with fewer distinct key topics addressed. Our large-scale patent citation analysis uncovers limited linkage between responsible AI research and the commercialization of AI technologies, suggesting that industry patents infrequently draw upon insights from the responsible AI literature. These patterns raise important questions about the integration of responsible AI considerations into commercialization practices, with potential implications for the alignment of AI development with broader societal objectives. Our results highlight the need for industry to publicly engage in responsible AI research to absorb academic knowledge, cultivate public trust, and proactively address the societal dimensions of AI development.