Context Collapse: Barriers to Adoption for Generative AI in Workplace Settings

TL;DR

This paper examines the challenges of integrating generative AI into workplaces, highlighting how current methods fail to consider user context and proposing more interactional approaches.

Contribution

It empirically analyzes how different stakeholders conceptualize context and identifies pitfalls in computational approaches to embedding AI in work environments.

Findings

Current AI tools often fail to accurately account for user context.

Multiple contexts tend to collapse or degrade, reducing utility.

A shift towards interactional practices is recommended for better integration.

Abstract

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.