What Should a Robot Do? Comparing Human and Large Language Model Recommendations for Robot Deception

Kantwon Rogers, Reiden John Allen Webber, Geronimo Gorostiaga Zubizarreta, Arthur Melo Cruz, Shengkang Chen, Ronald C. Arkin, Jason Borenstein, Alan R. Wagner

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study compares human ethical judgments with Large Language Models (LLMs) on robotic deception in various scenarios. Surveying human participants and querying LLMs, we presented ethical dilemmas in high-risk and low-risk contexts. Findings reveal alignment between humans and LLMs in high-risk scenarios, prioritizing safety, but notable divergences in low-risk situations, reflecting challenges in AI development to accurately capture human social nuances and moral expectations.

Original languageEnglish (US)
Title of host publicationHRI 2024 Companion - Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction
PublisherIEEE Computer Society
Pages906-910
Number of pages5
ISBN (Electronic)9798400703232
DOIs
StatePublished - Mar 11 2024
Event19th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2024 - Boulder, United States
Duration: Mar 11 2024Mar 15 2024

Publication series

NameACM/IEEE International Conference on Human-Robot Interaction
ISSN (Electronic)2167-2148

Conference

Conference19th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2024
Country/TerritoryUnited States
CityBoulder
Period3/11/243/15/24

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

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