arXiv:2603.02961v2 Announce Type: replace-cross Abstract: As AI systems enter institutional workflows, workers must decide whether to delegate task execution to AI and how much effort to invest in verifying AI outputs, while institutions evaluate workers using outcome-based standards that may misalign with workers' private costs. We model delegation and verification as the solution to a rational worker's optimization problem, and define worker quality by evaluating an institution-centered utility (distinct from the worker's objective) at the resulting optimal action. We formally characterize o
The increasing integration of AI into enterprise workflows necessitates formal models to understand its impact on human agency, decision-making, and organizational structures, especially as AI capabilities mature.
This research provides a framework for understanding the complex interplay between human workers, AI systems, and institutional incentives, offering insights into optimal delegation and verification strategies as AI becomes ubiquitous.
The formal modeling of worker-AI interaction under outcome-based evaluation highlights potential misalignments between worker incentives and institutional goals, shifting how we conceptualize 'worker quality' in AI-augmented environments.
- · AI ethics researchers
- · Organizational psychologists
- · Companies implementing AI with clear governance
- · Organizations without clear AI delegation policies
- · Workers unable to adapt to AI-augmented roles
Companies will develop more sophisticated frameworks for AI integration, including policies for delegation and verification.
New metrics for worker performance and quality will emerge that account for AI collaboration and oversight.
Labor markets will experience shifts as skills related to AI verification, delegation, and ethical oversight become highly valued.
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Read at arXiv cs.AI