# AI Usage Cards: Responsibly Reporting AI-generated Content

arXiv: https://arxiv.org/abs/2303.03886 · HTML: https://mlsanity.com/paper/2303.03886/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2023.

## Built on by (most cited first, 16 of 16)

- [A Survey on Detection of LLMs-Generated Content](https://mlsanity.com/paper/2310.15654/) (2023)
- [We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fields](https://mlsanity.com/paper/2310.14870/) (2023)
- [Mapping the Ethics of Generative AI: A Comprehensive Scoping Review](https://mlsanity.com/paper/2402.08323/) (2024)
- [Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges](https://mlsanity.com/paper/2405.15604/) (2024)
- [Use case cards: a use case reporting framework inspired by the European AI Act](https://mlsanity.com/paper/2306.13701/) (2023)
- [Paraphrase Detection: Human vs. Machine Content](https://mlsanity.com/paper/2303.13989/) (2023)
- [TrojanStego: Your Language Model Can Secretly Be A Steganographic Privacy Leaking Agent](https://mlsanity.com/paper/2505.20118/) (2025)
- [Citation Amnesia: On The Recency Bias of NLP and Other Academic Fields](https://mlsanity.com/paper/2402.12046/) (2024)
- [SPaRC: A Spatial Pathfinding Reasoning Challenge](https://mlsanity.com/paper/2505.16686/) (2025)
- [Unlocking Model Insights: A Dataset for Automated Model Card Generation](https://mlsanity.com/paper/2309.12616/) (2023)
- [Detecting AI Generated Text Based on NLP and Machine Learning Approaches](https://mlsanity.com/paper/2404.10032/) (2024)
- [Mind the Gap Between Spatial Reasoning and Acting! Step-by-Step Evaluation of Agents With Spatial-Gym](https://mlsanity.com/paper/2604.09338/) (2026)
- [Documentation Practices of Artificial Intelligence](https://mlsanity.com/paper/2406.18620/) (2024)
- [Joint Model Assignment and Resource Allocation for Cost-Effective Mobile Generative Services](https://mlsanity.com/paper/2409.09072/) (2024)
- [Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias](https://mlsanity.com/paper/2512.05714/) (2025)
- [MABPD: Multi-Agent Bias Probing & Detection via Structured Argument Debate](https://mlsanity.com/paper/2609.04841/) (2026)

## Abstract (arXiv)

Given AI systems like ChatGPT can generate content that is indistinguishable from human-made work, the responsible use of this technology is a growing concern. Although understanding the benefits and harms of using AI systems requires more time, their rapid and indiscriminate adoption in practice is a reality. Currently, we lack a common framework and language to define and report the responsible use of AI for content generation. Prior work proposed guidelines for using AI in specific scenarios (e.g., robotics or medicine) which are not transferable to conducting and reporting scientific research. Our work makes two contributions: First, we propose a three-dimensional model consisting of transparency, integrity, and accountability to define the responsible use of AI. Second, we introduce ``AI Usage Cards'', a standardized way to report the use of AI in scientific research. Our model and cards allow users to reflect on key principles of responsible AI usage. They also help the research community trace, compare, and question various forms of AI usage and support the development of accepted community norms. The proposed framework and reporting system aims to promote the ethical and responsible use of AI in scientific research and provide a standardized approach for reporting AI usage across different research fields. We also provide a free service to easily generate AI Usage Cards for scientific work via a questionnaire and export them in various machine-readable formats for inclusion in different work products at https://ai-cards.org.