Between Uncertainty and Familiarity: A Study on Office Workers’ Trust in AI

As Artificial Intelligence (AI) technology becomes more prevalent in professional occupations, it’s vital to comprehend the elements that shape how these workers interact with and trust AI systems. This study delves into how attributes resembling both the system and humans influence the way office workers view and interact with AI enabled workplace applications (AI-EWAs). Using Uncertainty Reduction Theory (URT), the Computer as Social Actors (CASA) model, and considering the Uncanny Valley Effect (UVE), we investigate how these system-like and human-like traits affect user experiences and trust among office workers. URT highlights how system-like features in AI reduce uncertainty for office workers, enhancing their understanding, decision-making, and data management. This fosters trust and reduces uncertainty. Concurrently, the UVE warns that as AI becomes more human-like, it can reach a point of discomfort and reduced trust. Designing AI applications requires careful balance to avoid triggering this effect. Conversely, the CASA model highlights the importance of human-like attributes, such as personification and social cues, in shaping how office workers perceive and interact with AI applications. CASA suggests that individuals frequently apply the same expectations and social norms from human-to-human interactions (HHI) to human-to-computer communications (HCI). Incorporating human-like traits in AI design fosters familiarity and social connection, promoting engagement, satisfaction, and trust among office workers while avoiding the UVE. By exploring both system-like and human-like features, along with understanding the UVE, this research aims to provide a comprehensive insight into factors that affect trust and interactions with AI-EWAs among office workers. Our findings will inform the design of AI systems that blend traits to enhance user experiences, trust, and acceptance in the workplace. This research offers insights for companies, solution architects, and HR managers implementing AI solutions, considering office workers’ perspectives and the implications of the UVE.