Signaling transparency in the era of artificial intelligence

Abstract

This study provides researchers and business practitioners with a comprehensive understanding of artificial intelligence (AI) transparency in the business discipline, enabling them to navigate the evolving digital landscape, where AI transparency is an escalating concern, by identifying the conceptual foundations in the most influential studies.
This study uses bibliometric analysis techniques, including performance and co-citation analyses. These analyses are grounded in data extracted from the Social Sciences Citation Index within the Web of Science, comprising 108 primary articles and 7,459 secondary cited studies.
AI transparency research is rising with a greater focus on end-users. Six clusters of cited publications serve as the bedrock of AI transparency in the business discipline: trust, AI explanation, bias and power, undesirable usage, user acceptance/aversion, and user heuristics. Analyzing these clusters revealed a framework for signaling AI transparency that can be extended to future research and business strategies.
This study addresses the following research gaps. First, the nature of AI transparency and its knowledge basis remain elusive. Second, AI transparency in the business discipline is under-explored compared to information sciences and law. Third, there is ambiguity surrounding the implementation strategies for AI transparency, with companies often resorting to simplistic methods such as updating terms and conditions. Fourth, there is a lack of clear future research directions specifically for AI transparency, as opposed to the broader context of AI ethics.

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ORCID for Carmen Lopez: orcid.org/0000-0002-5510-1920

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