This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: A new framework for thinking about artificial intelligence at work starts with a question companies have spent relatively little time asking. Companies are racing to build generative AI into everyday work, from drafting and data analysis to scheduling and decision support.
Vedant Das Swain of NYU Tandon and Koustuv Saha of the University of Illinois Urbana-Champaign argue that organizations should also prepare for the opposite possibility: a budget cut, outage, privacy rule, regulatory action or vendor dispute that suddenly makes AI unavailable. The researchers describe their Counterfactual Resilience Framework, or CReF, in a visionary paper they presented at the inaugural ACM AI Leadership Summit, which ran Aug. 30 to Sept. 2 in Atlanta. According to Das Swain, the paper is meant to inspire transformative research agendas and identify grand challenges through engagement with the entire AI ecosystem.
CReF proposes a way for organizations and researchers to expose dependencies that may be difficult to see while AI is working normally. The framework asks users to construct a "counterfactual anchor" with three parts: a workplace setting in which AI is being used, an event that removes access to it and the aftermath. The idea is to treat AI's absence as a kind of stress test.
"A lot of research on human-AI teaming and complementarity assumes that LLMs and generative AI tools are like oxygen," Das Swain said. "It will be abundant, uniformly accessible, and continually replenished. We wanted to challenge this assumption by urging organizations to ask how they would look if AI was suddenly snatched away.
Our framework helps stakeholders confront who looks productive, who retains expertise or whether basic work can continue in the face of a technological barrier, outage or attack. Existing AI productivity frameworks excite us by focusing on the best case. Instead, we shift attention to pragmatically planning for the worst case." The paper explores three potential vulnerabilities: whether AI benefits are distributed unevenly across jobs; whether prolonged AI use could erode skills workers need to operate independently; and whether organizations can recover when an AI-dependent workflow is disrupted.
To illustrate them, the authors construct fictional workplaces. In one, Alex manages complicated relationships and gets relatively little benefit from AI, while Sam works on a data analytics team whose output increases after adopting it. Management begins interpreting their differing output through an AI-influenced notion of productivity.
Extract — continue reading at the source.
