Context Collapse: Barriers to Adoption for Generative AI in Workplace Settings
This addresses the adoption barriers for generative AI in workplaces, but it is incremental as it builds on existing critiques of context in technology.
The paper tackles the problem of generative AI failing to account for user context in workplace settings, showing through expert interviews that context collapse reduces utility and users deploy strategies to address this, concluding with a call for more interactional practices.
As generative AI technologies are pressed into service in workplace settings, current approaches to account for the contexts in which such technologies are used fall short of users' expectations and needs. This paper empirically demonstrates, through expert interviews, both how these tools fail to account for users' context and how users deploy concrete strategies address such failures. The paper analyzes how context is variously conceptualized by tool developers, users, and social scientists to identify specific pitfalls inherent in computational approaches to context. Multiple distinct contexts tend to collapse into one another or rot, degrading over time, reducing the utility of any efforts to account for context. The paper concludes with a provocation to shift from an indiscriminate collection of context-relevant data toward a more interactional set of practices to embed GenAI systems more appropriately into users' contexts of use.