The U.S. nuclear industry is increasingly modernizing its operations by integrating automation technologies to improve efficiency, safety, and reliability, while minimizing unnecessary costs. However, successfully deploying automation technologies for operations and maintenance (O&M) in Nuclear Power Plants (NPPs) requires addressing several critical challenges, including ensuring that the automation is trustworthy, transparent, and operationally acceptable. This paper focuses on automation transparency, a characteristic that plays a vital role in enabling safe and efficient human-automation interactions and, thus, contributing effectively to operational risk management. Despite its importance, there is currently no consensus on the definition of automation transparency across various domains, and existing methodologies for measuring transparency are predominantly subjective and context specific. This paper presents a literature review to assess the existing definitions and methodologies of automation transparency and identifies key limitations in their applicability to the nuclear domain. In addressing these limitations, this study proposes a novel definition that refers to automation transparency as “the degree to which underlying information about the inner workings of an automation system is conveyed, relevant to its intended use.” Aligning with this proposed definition, three evaluation approaches for automation transparency, namely attribute-based, model-replication-based, and entropy-based, are developed. A hypothetical case study that involves an Artificial Intelligence (AI)-based automated anomaly detection system used in NPP monitoring is leveraged to test these three evaluation approaches to better understand their feasibility, applicability, and limitations. Future work will apply the lessons learned from this study for a practical case study to further understand the impacts of automation transparency on human performance.
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