# Beyond principlism: Practical strategies for ethical AI use in research practices

arXiv: https://arxiv.org/abs/2401.15284 · HTML: https://mlsanity.com/paper/2401.15284/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 70 references that are in the map.
- First posted on arXiv 27 Jan 2024.

## Builds on (most cited first, 11 of 11)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Bias and Fairness in Large Language Models: A Survey](https://mlsanity.com/paper/2309.00770/) (2023)
- [Can large language models provide useful feedback on research papers? A large-scale empirical analysis](https://mlsanity.com/paper/2310.01783/) (2023)
- [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019)
- [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019)
- [Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support](https://mlsanity.com/paper/2203.15144/) (2022)
- [Bias of AI-Generated Content: An Examination of News Produced by Large Language Models](https://mlsanity.com/paper/2309.09825/) (2023)
- [Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governance](https://mlsanity.com/paper/2206.11922/) (2022)
- [Friend or Foe? Exploring the Implications of Large Language Models on the Science System](https://mlsanity.com/paper/2306.09928/) (2023)
- [Techniques for supercharging academic writing with generative AI](https://mlsanity.com/paper/2310.17143/) (2023)
- [Social Construction of XAI: Do We Need One Definition to Rule Them All?](https://mlsanity.com/paper/2211.06499/) (2022)

## Built on by (most cited first, 7 of 7)

- [Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents](https://mlsanity.com/paper/2503.24047/) (2025)
- [Hidden Prompts in Manuscripts Exploit AI-Assisted Peer Review](https://mlsanity.com/paper/2507.06185/) (2025)
- [A validity-guided workflow for robust large language model research in psychology](https://mlsanity.com/paper/2507.04491/) (2025)
- [Cryptographic Runtime Governance for Autonomous AI Systems: The Aegis Architecture for Verifiable Policy Enforcement](https://mlsanity.com/paper/2603.16938/) (2026)
- [Generative AI Policies under the Microscope: How CS Conferences Are Navigating the New Frontier in Scholarly Writing](https://mlsanity.com/paper/2410.11977/) (2024)
- [The why, what, and how of AI-based coding in scientific research](https://mlsanity.com/paper/2410.02156/) (2024)
- [Generative Artificial Intelligence for Academic Research: Evidence from Guidance Issued for Researchers by Higher Education Institutions in the United States](https://mlsanity.com/paper/2503.00664/) (2025)

## Abstract (arXiv)

The rapid adoption of generative artificial intelligence (AI) in scientific research, particularly large language models (LLMs), has outpaced the development of ethical guidelines, leading to a "Triple-Too" problem: too many high-level ethical initiatives, too abstract principles lacking contextual and practical relevance, and too much focus on restrictions and risks over benefits and utilities. Existing approaches--principlism (reliance on abstract ethical principles), formalism (rigid application of rules), and technological solutionism (overemphasis on technological fixes)--offer little practical guidance for addressing ethical challenges of AI in scientific research practices. To bridge the gap between abstract principles and day-to-day research practices, a user-centered, realism-inspired approach is proposed here. It outlines five specific goals for ethical AI use: 1) understanding model training and output, including bias mitigation strategies; 2) respecting privacy, confidentiality, and copyright; 3) avoiding plagiarism and policy violations; 4) applying AI beneficially compared to alternatives; and 5) using AI transparently and reproducibly. Each goal is accompanied by actionable strategies and realistic cases of misuse and corrective measures. I argue that ethical AI application requires evaluating its utility against existing alternatives rather than isolated performance metrics. Additionally, I propose documentation guidelines to enhance transparency and reproducibility in AI-assisted research. Moving forward, we need targeted professional development, training programs, and balanced enforcement mechanisms to promote responsible AI use while fostering innovation. By refining these ethical guidelines and adapting them to emerging AI capabilities, we can accelerate scientific progress without compromising research integrity.