L Chen: Researcher — Prolific Citations Library

Explore 2 peer-reviewed studies by L Chen in Socio-Economic Impacts of AI and Algorithmic Systems (2024–2026). Discover research powered by Prolific's participant panel.

This page lists 2 peer-reviewed papers authored or co-authored by L Chen in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (2 of 2)

Once One Fails, All Are Suspect: Understanding Error Generalization in AI

Authors: L Dai, Z Wang, L Chen, J Jin

Year: 2026

Published in: 2026•scholarspace.manoa.hawaii.edu

Institution: Shanghai International Studies University

Research Area: Socio-Economic Impacts of AI, Algorithmic Systems

Discipline: Computer Science, Artificial Intelligence

AI errors lead to broader negative generalizations about other AI systems compared to human errors, largely due to perceptions of AI's inflexibility and inability to learn from mistakes.

Methods: Conducted four one-factor experiments across distinct contexts to compare human responses to AI errors and human errors.

Key Findings: Generalization of error perceptions from one AI system to others, and psychological mechanisms driving this process.
Overcoming algorithm aversion: A comparison between process and outcome control

Authors: L Cheng, A Chouldechova

Year: 2024

Published in: Proceedings of the 2023 CHI Conference ..., 2023 - dl.acm.org

Institution: Carnegie Mellon University

Research Area: Human-Computer Interaction (HCI), Algorithm Aversion, Decision Science

Discipline: Human-Computer Interaction (HCI)

Giving users process control by selecting the training algorithm mitigates algorithm aversion, but not by changing input factors, while combined outcome and process control is not more effective than each individually.

Methods: Replication study on outcome control and novel process control conditions tested on MTurk and Prolific platforms.

Key Findings: Impact of outcome control, process control, and combined controls on algorithm aversion mitigation.

DOI: https://doi.org/10.1145/3544548.3581253

Citations: 41