The Narrow Depth and Breadth of Corporate Responsible AI Research
<|reference_start|>The Narrow Depth and Breadth of Corporate Responsible AI Research: 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 critical 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 findings reveal that the majority of AI firms show limited or no engagement in this critical subfield of AI. We show a stark disparity between industry's dominant presence in conventional AI research and its limited 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 documents a narrower scope of responsible AI research within industry, with a lack of diversity in key topics addressed. Our large-scale patent citation analysis uncovers a pronounced disconnect between responsible AI research and the commercialization of AI technologies, suggesting that industry patents rarely build upon insights generated by the responsible AI literature. This gap highlights the potential for AI development to diverge from a socially optimal path, risking unintended consequences due to insufficient consideration of ethical and societal implications. Our results highlight the urgent need for industry to publicly engage in responsible AI research to absorb academic knowledge, cultivate public trust, and proactively mitigate AI-induced societal harms.<|reference_end|>
@article{ahmed2024the,
    title={The Narrow Depth and Breadth of Corporate Responsible AI Research},
    author={Nur Ahmed, Amit Das, Kirsten Martin and Kawshik Banerjee},
    journal={arXiv preprint arXiv:2405.12193},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.12193},
    primaryClass={cs.CY}
}