A Survey of AI Reliance

2024-07-22

arXiv.org

AI Summary

Current AI dependency research lags behind technological deployment, suffering from conceptual ambiguity, inconsistent measurement approaches, insufficient longitudinal designs, and limited external validity. To address these gaps, this study employs a systematic literature review combined with morphological analysis, integrating perspectives from psychology, human–computer interaction, and AI ethics. It introduces the first interdisciplinary morphological matrix framework for AI dependency, unifying core dimensions—including dependency object, context, intensity, and consequences—as well as standardized measurement paradigms. The framework innovatively identifies emerging research directions, such as generative AI dependency and multi-user collaborative dependency, and delivers an actionable research roadmap. By establishing conceptual consensus, methodological standards, and empirically grounded pathways, this work advances the systematic, standardized, and theoretically rigorous development of AI dependency research.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Other Foundations of Human Computation & AI
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Artificial intelligence (AI) systems have become an indispensable component of modern technology. However, research on human behavioral responses is lagging behind, i.e., the research into human reliance on AI advice (AI reliance). Current shortcomings in the literature include the unclear influences on AI reliance, lack of external validity, conflicting approaches to measuring reliance, and disregard for a change in reliance over time. Promising avenues for future research include reliance on generative AI output and reliance in multi-user situations. In conclusion, we present a morphological box that serves as a guide for research on AI reliance.

Problem

• Addresses research gap in human-AI reliance studies
• Proposes sociotechnical framework for understanding AI reliance
• Identifies core influences and future research directions

Innovation

• Sociotechnical perspective on AI reliance
• Morphological box categorization framework
• Core influences within sociotechnical system components