Revisiting the trolley problem for AI: biases and stereotypes in Large Language Models and their impact on ethical decision-making
Loading...
Author (Corporation)
Publication date
2025
Typ of student thesis
Course of study
Collections
Type
04B - Conference paper
Editors
Editor (Corporation)
Supervisor
Parent work
Proceedings of the 2025 AAAI Spring Symposium Series
Special issue
DOI of the original publication
Link
Series
Series number
Volume
Issue / Number
Pages / Duration
213-219
Patent number
Publisher / Publishing institution
AAAI Press
Place of publication / Event location
San Francisco
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
The trolley problem has long served as a lens for exploring moral decision-making, now gaining renewed significance in the context of artificial intelligence (AI). This study investigates ethical reasoning in three open-source large language models (LLMs)—LLaMA, Mistral and Qwen—through variants of the trolley problem. By introducing demographic prompts (age, nationality and gender) into three scenarios (switch, loop and footbridge), we systematically evaluate LLM responses against human survey data from the Moral Machine experiment. Our findings reveal notable differences: Mistral exhibits a consistent tendency to overintervene, while Qwen chooses to intervene less and LLaMA balances between the two. Notably demographic attributes, particularly nationality, significantly influence LLM decisions, exposing potential biases in AI ethical reasoning. These insights underscore the necessity of refining LLMs to ensure fairness and ethical alignment, leading the way for more trustworthy AI systems.
Keywords
Event
2025 AAAI Spring Symposium
Exhibition start date
Exhibition end date
Conference start date
31.03.2025
Conference end date
02.04.2025
Date of the last check
ISBN
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Closed
License
Citation
Hatemo, S., Weickhardt, C., Gisler, L., & Bendel, O. (2025). Revisiting the trolley problem for AI: biases and stereotypes in Large Language Models and their impact on ethical decision-making. In R. Petrick & C. Geib (Eds.), Proceedings of the 2025 AAAI Spring Symposium Series (pp. 213–219). AAAI Press. https://doi.org/10.1609/aaaiss.v5i1.35590