# Regulatory gray areas of LLM Terms

arXiv: https://arxiv.org/abs/2601.08415 · HTML: https://mlsanity.com/paper/2601.08415/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 13 Jan 2026.

## Builds on (most cited first, 9 of 9)

- [Sentiment Analysis in the Era of Large Language Models: A Reality Check](https://mlsanity.com/paper/2305.15005/) (2023)
- [Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers](https://mlsanity.com/paper/2504.18412/) (2025)
- [Scaling Trends for Data Poisoning in LLMs](https://mlsanity.com/paper/2408.02946/) (2024)
- [Privacy Policies over Time: Curation and Analysis of a Million-Document Dataset](https://mlsanity.com/paper/2008.09159/) (2020)
- [Everything Everywhere All at Once: LLMs can In-Context Learn Multiple Tasks in Superposition](https://mlsanity.com/paper/2410.05603/) (2024)
- [Large Models of What? Mistaking Engineering Achievements for Human Linguistic Agency](https://mlsanity.com/paper/2407.08790/) (2024)
- [The Accountability Paradox: How Platform API Restrictions Undermine AI Transparency Mandates](https://mlsanity.com/paper/2505.11577/) (2025)
- [Between Help and Harm: An Evaluation of Mental Health Crisis Handling by LLMs](https://mlsanity.com/paper/2509.24857/) (2025)
- [ChatGPT or A Silent Everywhere Helper: A Survey of Large Language Models](https://mlsanity.com/paper/2503.17403/) (2025)

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

- [OPEN-1B: A Fully Auditable Training Run](https://mlsanity.com/paper/2609.17380/) (2026)
- [An Explainable Agentic System for Detection of Conversational Scams with Summary-Based Memory](https://mlsanity.com/paper/2607.11707/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are increasingly integrated into academic research pipelines; however, the Terms of Service governing their use remain under-examined. We present a comparative analysis of the Terms of Service of five major LLM providers (Anthropic, DeepSeek, Google, OpenAI, and xAI) collected in November 2025. Our analysis reveals substantial variation in the stringency and specificity of usage restrictions for general users and researchers. We identify specific complexities for researchers in security research, computational social sciences, and psychological studies. We identify `regulatory gray areas' where Terms of Service create uncertainty for legitimate use. We contribute a publicly available resource comparing terms across platforms (OSF) and discuss implications for general users and researchers navigating this evolving landscape.