# Science in the Era of ChatGPT, Large Language Models and Generative AI: Challenges for Research Ethics and How to Respond

arXiv: https://arxiv.org/abs/2305.15299 · HTML: https://mlsanity.com/paper/2305.15299/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 49 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 10 of 10)

- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Scaling Laws for Reward Model Overoptimization](https://mlsanity.com/paper/2210.10760/) (2022)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT](https://mlsanity.com/paper/2303.04226/) (2023)
- [Categorizing Variants of Goodhart's Law](https://mlsanity.com/paper/1803.04585/) (2018)
- [Prompting Large Language Models With the Socratic Method](https://mlsanity.com/paper/2303.08769/) (2023)
- [The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies](https://mlsanity.com/paper/2212.08104/) (2022)
- [How Value-Sensitive Design Can Empower Sustainable Consumption](https://mlsanity.com/paper/2004.09180/) (2020)
- [Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence](https://mlsanity.com/paper/2301.05995/) (2023)

## Built on by (most cited first, 2 of 2)

- [Generative AI voting: fair collective choice is resilient to LLM biases and inconsistencies](https://mlsanity.com/paper/2406.11871/) (2024)
- [The use of GPT-4o and Other Large Language Models for the Improvement and Design of Self-Assessment Scales for Measurement of Interpersonal Communication Skills](https://mlsanity.com/paper/2409.14050/) (2024)

## Abstract (arXiv)

Large language models of artificial intelligence (AI), such as ChatGPT, find remarkable but controversial applicability in science and research. This paper reviews epistemological challenges, ethical and integrity risks in science conduct in the advent of generative AI. This is with the aim to lay new timely foundations for a high-quality research ethics review. The role of AI language models as a research instrument and subject is scrutinized along with ethical implications for scientists, participants and reviewers. New emerging practices for research ethics review are discussed, concluding with ten recommendations that shape a response for a more responsible research conduct in the era of AI.