Innovation in Artificial Intelligence and the Catalyst of Open Data Sharing: Literature Review and Policy implications

John Dam and Henry Rickon

No a3zwu, Thesis Commons from Center for Open Science

Abstract: This literature review aims to elucidate the nuanced relationship between data openness and innovation within the field of Artificial Intelligence (AI). As the significance of AI continues to expand across various sectors, understanding the role of open data in fostering innovation becomes increasingly critical. Through this review, we systematically explore and analyze the wealth of existing literature on the topic. We address key concepts, theoretical perspectives, and empirical findings, shedding light on the multi-dimensional facets of data openness, including accessibility and usability, and their impact on AI innovation. Furthermore, the review highlights the practical implications and potential strategies to leverage data openness in propelling AI innovation. We also identify existing gaps and limitations in current literature, suggesting avenues for future research. This comprehensive review contributes to the evolving discourse in AI studies, offering valuable insights to researchers, data managers, and AI practitioners alike.

Date: 2023-05-15
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cse, nep-ino, nep-sbm and nep-tid
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Persistent link: https://EconPapers.repec.org/RePEc:osf:thesis:a3zwu

DOI: 10.31219/osf.io/a3zwu

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