Worker-Centric AI for Decision Support

Abstract

Just as the Industrial Revolution reshaped manual labor, AI technologies are now transforming cognitive work—offering intelligent support and fundamentally changing the nature of workers' tasks and workflows. The design of AI-powered decision-support systems affects not only output-centric values of work --- such as quality, efficiency, and creativity ---but also human-centric values, including workers' skills in AI-supported tasks, their agency, collaboration, and the meaning they derive from their work. However, current AI decision-support paradigms typically focus only on output-centric metrics (e.g., decision accuracy) and overlook how people process information, their motivation to engage with AI recommendations, and their ability to critically assess AI outputs. As a result, these systems often lead to overreliance, fail to enable human-AI complementarity, and may even deskill workers. For both moral and economic reasons, AI tools must empower workers, help them develop new skills, and truly complement human expertise. Technologies that support worker agency and skill development are more likely to lead to long-term organizational performance, job satisfaction, and economic resilience. To achieve this, I argue that AI systems must be designed from a worker-centric perspective --- one that optimizes both output- and human-centric outcomes and is grounded in human cognition: how people think, decide, learn, and apply expertise. In the first part of this dissertation, I demonstrate that existing AI decision-support systems do not sufficiently account for human cognition. They are built on the implicit --- yet incorrect --- assumption that users consistently engage cognitively with AI support. Challenging this assumption within the field of human-AI decision-making, I show that cognitive engagement is an essential mechanism for critically evaluating and effectively incorporating AI advice into decision-making. Drawing on the dual-process theory of cognition, I demonstrate that current paradigms of AI support exacerbate heuristic (System 1) thinking by offering readily available decisions and explanations that users can adopt with minimal effort. To counteract this, I introduce cognitive forcing functions ---interaction interventions that elicit cognitive engagement by disrupting heuristic processing at decision time --- and show that these significantly reduce overreliance on AI. Building on these findings, in the second part of this dissertation, I introduce a suite of novel systems that operationalize worker-centric AI --- systems that are grounded in human cognition when optimizing output- and human-centric outcomes of work. I present AI decision-support systems that: (1) complement human judgment through adaptive AI support policies, learned via reinforcement learning, that personalize assistance based on contextual and cognitive factors; (2) augment human skills through human-centered contrastive explanations that address knowledge gaps by contrasting AI decisions with likely human reasoning; and (3) extend human perspective-taking capabilities in decisions requiring cognitive empathy by simulating diverse viewpoints, as demonstrated with the AHA! system for AI deployment decisions. Together, these contributions advance the field of human-AI decision-making and chart a path toward worker-centric AI systems --- designed not only to enhance productivity but also to sustain workforce development. Such systems ensure that AI complement human expertise, preserves pathways for skill acquisition, and ultimately strengthens the workforce.