ChatGPT’s inconsistent moral advice influences users’ judgment

Sebastian Krügel · Andreas Ostermaier · Matthias Uhl

183 citations0/0 files storedScientific Reports

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Ethics and Social Impacts of AI Ethics in Business and Education Psychology of Moral and Emotional Judgment

Abstract

ChatGPT is not only fun to chat with, but it also searches information, answers questions, and gives advice. With consistent moral advice, it can improve the moral judgment and decisions of users. Unfortunately, ChatGPT's advice is not consistent. Nonetheless, it does influence users' moral judgment, we find in an experiment, even if they know they are advised by a chatting bot, and they underestimate how much they are influenced. Thus, ChatGPT corrupts rather than improves its users' moral judgment. While these findings call for better design of ChatGPT and similar bots, we also propose training to improve users' digital literacy as a remedy. Transparency, however, is not sufficient to enable the responsible use of AI.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 183 citations exist upstream.

References

1976Killing, Letting Die, and the Trolley Problem 2021Bad machines corrupt good morals

Cited by

2024The usefulness of ChatGPT for psychotherapists and patients 2024A new sociology of humans and machines 2023Living guidelines for generative AI — why scientists must oversee its use 2024Navigating the perils of artificial intelligence: a focused review on ChatGPT and responsible research and innovation 2024The moral machine experiment on large language models 2024The impact of intelligent decision-support systems on humans' ethical decision-making: A systematic literature review and an integrated framework 2024Large language models can outperform humans in social situational judgments 2025Characteristics of ChatGPT users from Germany: Implications for the digital divide from web tracking data

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.