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  4. The Cost of Convenience: How Delegation and Decision Horizons Erode Human Agency when overusing LLM Systems
 
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2026
Conference Paper
Title

The Cost of Convenience: How Delegation and Decision Horizons Erode Human Agency when overusing LLM Systems

Abstract
Large Language Models (LLMs) are rapidly embedded in everyday interfaces as assistants, copilots, and autonomous agents that influence human decision-making. Prior research primarily evaluates these systems through accuracy, alignment, and task performance, assuming that agency is preserved as long as users retain formal control. This assumption cannot explain how agency erodes without obligation or manipulation. We present a conceptual framework that treats LLMs as delegated decision infrastructures to which users repeatedly offload decisions for convenience. We introduce decision horizons, defined as the set of options and framings made epistemically visible by LLMs, as a central analytic concept. Repeated delegation reshapes these horizons, increases opt-out friction, and raises the cost of reversibility. Rather than removing control, LLMs render agency latent by restructuring decision conditions. We conceptualize agency erosion as an emergent, cumulative interaction effect and outline design implications that foreground reversibility, horizon diversity, and long-term human agency.
Author(s)
Schlonsak, Ruben
Fraunhofer-Einrichtung für Individualisierte Medizintechnik IMTE  
Jetter, Hans-Christian
Universität zu Lübeck
Matthies, Denys
Fraunhofer-Einrichtung für Individualisierte Medizintechnik IMTE  
Mainwork
CHI 2026, CHI Conference on Human Factors in Computing Systems. Extended Abstracts  
Conference
Conference on Human Factors in Computing Systems 2026  
Open Access
File(s)
Download (4.26 MB)
Rights
CC BY 4.0: Creative Commons Attribution
DOI
10.1145/3772363.3798760
10.24406/publica-8643
Additional link
Full text
Language
English
Fraunhofer-Einrichtung für Individualisierte Medizintechnik IMTE  
Keyword(s)
  • Agency

  • Decision-Making

  • Delegation

  • Human-AI Interaction

  • Infrastructure

  • Large Language Models

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