# Ethical ChatGPT: Concerns, Challenges, and Commandments

arXiv: https://arxiv.org/abs/2305.10646 · HTML: https://mlsanity.com/paper/2305.10646/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 18 May 2023.

## Builds on (most cited first, 6 of 6)

- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021)
- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era](https://mlsanity.com/paper/2304.06488/) (2023)
- [Perception, performance, and detectability of conversational artificial intelligence across 32 university courses](https://mlsanity.com/paper/2305.13934/) (2023)
- [The moral authority of ChatGPT](https://mlsanity.com/paper/2301.07098/) (2023)

## Built on by (most cited first, 14 of 14)

- [Fairness of ChatGPT](https://mlsanity.com/paper/2305.18569/) (2023)
- [Bias and Fairness in Chatbots: An Overview](https://mlsanity.com/paper/2309.08836/) (2023)
- [Are Chatbots Ready for Privacy-Sensitive Applications? An Investigation into Input Regurgitation and Prompt-Induced Sanitization](https://mlsanity.com/paper/2305.15008/) (2023)
- [Review of Generative AI Methods in Cybersecurity](https://mlsanity.com/paper/2403.08701/) (2024)
- [Public Perceptions of Gender Bias in Large Language Models: Cases of ChatGPT and Ernie](https://mlsanity.com/paper/2309.09120/) (2023)
- [FLEX: A Benchmark for Evaluating Robustness of Fairness in Large Language Models](https://mlsanity.com/paper/2503.19540/) (2025)
- [MOKA: Moral Knowledge Augmentation for Moral Event Extraction](https://mlsanity.com/paper/2311.09733/) (2023)
- [Narrative-of-Thought: Improving Temporal Reasoning of Large Language Models via Recounted Narratives](https://mlsanity.com/paper/2410.05558/) (2024)
- [From Passive Watching to Active Learning: Empowering Proactive Participation in Digital Classrooms with AI Video Assistant](https://mlsanity.com/paper/2409.15843/) (2024)
- [Fine-Tuning Language Models for Scientific Writing Support](https://mlsanity.com/paper/2306.10974/) (2023)
- [Stability Analysis of ChatGPT-based Sentiment Analysis in AI Quality Assurance](https://mlsanity.com/paper/2401.07441/) (2024)
- [LAPIS: Language Model-Augmented Police Investigation System](https://mlsanity.com/paper/2407.20248/) (2024)
- [Exploring the effectiveness of ChatGPT-based feedback compared with teacher feedback and self-feedback: Evidence from Chinese to English translation](https://mlsanity.com/paper/2309.01645/) (2023)
- [The Granularity Gap: A Multi-Dimensional Cross-Generational Audit of Sycophancy in Gemini Models](https://mlsanity.com/paper/2606.05183/) (2026)

## Abstract (arXiv)

Large language models, e.g. ChatGPT are currently contributing enormously to make artificial intelligence even more popular, especially among the general population. However, such chatbot models were developed as tools to support natural language communication between humans. Problematically, it is very much a ``statistical correlation machine" (correlation instead of causality) and there are indeed ethical concerns associated with the use of AI language models such as ChatGPT, such as Bias, Privacy, and Abuse. This paper highlights specific ethical concerns on ChatGPT and articulates key challenges when ChatGPT is used in various applications. Practical commandments for different stakeholders of ChatGPT are also proposed that can serve as checklist guidelines for those applying ChatGPT in their applications. These commandment examples are expected to motivate the ethical use of ChatGPT.