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Washington University In St Louis Papers

0 research papers from Washington University In St Louis that used Prolific for participant recruitment.

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Behavioral Science

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Behavioral Science

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Washington University In St Louis

Washington University In St Louis: Research Institution — Prolific Citations Library

Browse 2 peer-reviewed papers from Washington University In St Louis spanning Human-AI Interaction, Cognitive Science (2024–2025). Research powered by Prolific's high-quality participant data.

This page lists 2 peer-reviewed papers from researchers at Washington University In St Louis in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (2 of 2)

Do People Think Fast or Slow When Training AI?

Authors: L S. Treiman, CJ Ho, W Kool

Year: 2025

Published in: Proceedings of the 2025 ACM Conference ..., 2025 - dl.acm.org

Institution: Washington University in St. Louis, National Cheng Kung University

Research Area: Human-AI Interaction, Cognitive Science, Behavioral Research in AI Training

Discipline: Human-Computer Interaction, Behavioral Science

Participants tend to rely on intuition (fast thinking) rather than deliberation (slow thinking) when training AI agents in the ultimatum game, impacting human-AI collaboration system design.

Methods: Participants trained an AI agent in the ultimatum game to analyze whether their training decisions aligned more with intuitive or deliberative cognitive processes.

Key Findings: The cognitive processes (fast vs. slow thinking) underlying human decision-making during AI training.

DOI: https://dl.acm.org/doi/abs/10.1145/3715275.3732177

Citations: 1
The consequences of AI training on human decision-making

Authors: LS Treiman, CJ Ho, W Kool

Year: 2024

Published in: Proceedings of the National Academy of ..., 2024 - pnas.org

Institution: Massachusetts Institute of Technology, Yale University, Washington University in St. Louis

Research Area: AI Ethics, Behavioral Economics, Decision-Making in AI Systems

Discipline: Artificial Intelligence, Behavioral Science

People alter their behavior when they know their actions will train AI, leading to unintentional habits and biased training data for AI systems.

Methods: Five studies were conducted using the ultimatum game; participants were tasked with deciding on monetary splits proposed by either humans or AI, with some informed their decisions would train the AI.

Key Findings: Behavioral changes in participants when training AI, persistence of these changes over time, and implications for AI training bias.

DOI: https://doi.org/10.1073/pnas.2408731121

Citations: 13