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AI Usage Cards: Responsibly Reporting AI-generated Content

Jan Philip Wahle, Terry Ruas, Saif M. Mohammad, Norman Meuschke, Bela Gipp

arXiv:2303.03886v2cs.CY

TL;DR

Scientific use of AI-generated content is growing amid unresolved concerns and a lack of shared reporting standards. The paper proposes a transparency, integrity, and accountability model alongside AI Usage Cards for standardized reporting. It presents the cards as a way to report AI use across scientific work, while noting limits in how many reporters the current version supports.

  • Problem

    There is no common framework or language for defining and reporting responsible AI-generated content use in scientific work.

    Source: §1 · pp. 1–2

  • Method

    The paper proposes a three-dimensional model of transparency, integrity, and accountability and introduces AI Usage Cards to report AI use in scientific work.

    Source: §1 · p. 2

  • Results

    The authors present AI Usage Cards as a standardized way to report AI use across steps of scientific work and as adaptable beyond scientific projects.

    Source: §1 · p. 2, §6 · p. 5

  • Takeaways & Limitations

    The authors frame responsible AI use as requiring transparency, integrity, and accountability together, with reporting intended to facilitate documenting AI use.

    Source: §3 · p. 3, §6 · p. 5

    The current version supports only a limited number of reporters, so focal points or group leaders should report their team’s actions.

    Source: §6 · p. 5

Abstract

from arXiv · show

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.

1 INTRODUCTION

The rapid adoption of AI-generated content has outpaced shared norms for its responsible use in scientific work. The paper proposes a three-dimensional model and AI Usage Cards to define and report that use, while emphasizing that context and stakes matter.

1 INTRODUCTION

  • AI-generated outputs may be false or fail to attribute sources, leaving it unclear whether models should be used for content generation.Researchers have also begun using AI for software development and academic writing while norms remain unsettled.Source: §1 · p. 1
  • Existing policies differ, while no common language or standardized framework reports the use of AI-generated content.The ACL policy calls for transparency, while ICML prohibited language-model use for submissions in 2023.Source: §1 · pp. 1–2
  • AI Usage Cards are designed to acknowledge AI support across scientific-process phases and remain adaptable across domains.The cards can be generated through an online questionnaire and exported in machine-readable formats for scientific work.Source: §1 · p. 2
  • The paper proposes a model for responsible AI use in science based on transparency, integrity, and accountability.It also introduces AI Usage Cards as a standardized tool for reporting AI use.Source: §1 · p. 2
  • Responsible use is context-dependent: scientific applications range from low-stakes tasks to high-stakes medical or legal scenarios with potential for harm.The authors call for caution and consideration of potential consequences in high-stakes cases.Source: §1 · p. 2

2 RELATED WORK

Prior work addresses AI detection and domain-specific reporting, while existing cards document models, datasets, and evaluations. The paper distinguishes AI Usage Cards as a way to report AI-generated content use.

2 RELATED WORK

  • Detection methods do not resolve integrity and accountability concerns, and humans are increasingly unable to detect AI-generated content [4].Prior efforts include zero-shot detection [12] and text watermarking [10].Source: §2 · p. 2
  • Health-domain guidance addresses diagnosis, trials, and research, but the cited contributions may not transfer across domains or report which models and textual changes were used.The cited work also highlights accountability and the risks of biased or unverified information.Source: §2 · p. 2
  • Data Cards document dataset characteristics [19], Model Cards document model details such as training data and parameters [13], and Evaluation Cards standardize experiments [6].Evaluation Cards support monitoring trends in NLP research [6].Source: §2 · pp. 2–3

3 A THREE-DIMENSIONAL MODEL

The model frames responsible AI use through transparency, integrity, and accountability. It applies across research domains, with each dimension addressing a distinct part of documenting, assessing, and taking responsibility for AI-assisted content.

3 A THREE-DIMENSIONAL MODEL

  • The three pillars call for acknowledging AI use, approving generated content, and clarifying who is responsible for it.The model is presented as general enough for many research domains, unlike frameworks that do not specify which parts of scientific work AI contributed to.Source: §3 · p. 3
  • Transparency means documenting AI use throughout a project so readers can trace which scientific-process steps AI contributed to.The authors also describe this documentation as helping authors reflect on which parts of their work originated from AI.Source: §3 · p. 3
  • Integrity assessment concerns correctness, truthfulness, fairness, safety, and appropriateness, and the authors recommend human assessment and documentation.For smaller initiatives, they recommend input from diverse people; for larger projects, participatory-design strategies.Source: §3 · p. 3
  • Accountability includes proactively ensuring integrity before release, responding to feedback, and documenting people who can be contacted about issues.The authors distinguish accountability from transparency: knowing what was manipulated does not itself establish liability or a route to address harm.Source: §3 · p. 3
  • The authors say all three dimensions must be fulfilled simultaneously for responsible AI use; omitting one can leave inaccuracies, bias, or problems without accountability.They describe acknowledging use, mitigating inappropriate outputs, and assigning someone to respond as necessary to mitigate major issues.Source: §3 · p. 3

4 AI USAGE CARDS

AI Usage Cards standardize reporting of AI assistance in scientific work around transparency, integrity, and accountability. Their structure covers the scientific process and can be adapted across domains.

4 AI USAGE CARDS

  • AI Usage Cards provide a standardized practice for documenting responsible AI use through transparency, integrity, and accountability.The cards are motivated by scientific work but designed to be adaptable to other domains.Source: §4 · p. 3
  • The cards follow the scientific method and can be adapted for different domains, disciplines, stakeholders, and levels of detail.Organizations can define questions according to what they consider necessary for responsible AI use in their scenarios.Source: §4 · p. 3

4.1 Structure

  • Six blocks cover project details, ideation and review, methodology and experiments, writing and presentation, code and data, and ethics.Together, these blocks address AI assistance from theoretical ideation through practical work and communication of results.Source: §4.1 · p. 4
  • Subcategories classify AI-supported content as new, revised from prior content, or compared across multiple pieces of content.The distinctions depend on whether content is generated from prompts, based on prior ideas, or produced by comparing content.Source: §4.1 · p. 4
  • A free online questionnaire generates cards for inclusion in scientific reports, with exports available in machine-readable formats.Formats include LaTeX, XML, JSON, and CSV, supporting automated analysis across domains and content types.Source: §4 · p. 3, §4.1 · p. 4

5 PRACTICAL CONSIDERATIONS

The practical guidance frames cards as a way to document AI support across content-related activities, while encouraging reflection rather than justifying harmful dissemination. It also addresses adaptability, adoption, and review.

5 PRACTICAL CONSIDERATIONS

  • AI Usage Cards should document AI support for content-related activities across projects, including scientific papers and blog posts.Users categorize each supported activity according to the card.Source: §5 · p. 4
  • The cards encourage reflection, acknowledgment, attention to bias, and machine-readable reporting as contributions to responsible AI use.The paper also describes these practices as supporting transparency, accountability, and integrity.Source: §5 · p. 4
  • Cards should not justify unethical or inappropriate dissemination; users should reflect on generated content and its value to society.Source: §5 · p. 4
  • Cards should be revised periodically as technologies change and domains have varying requirements.The paper notes that concerns about large language models may change over time.Source: §5 · p. 4
  • Different card versions can serve different scientific works and other areas, with questions tailored to varying needs.The paper gives dissertations and short papers as examples of works that may need different levels of detail.Source: §5 · p. 4
  • Simpler, use-case-specific formats or reporting requirements for scientific submissions could encourage card adoption.The cards are described as enabling efficient, standardized, machine-processable reports.Source: §5 · p. 4
  • Cards can help conference and journal organizers understand AI support, but organizers must clarify acceptable assistance thresholds.Source: §5 · p. 5
  • A free, interactive questionnaire can generate cards in less than five minutes.Source: §5 · p. 5

6 DISCUSSION

The discussion presents AI Usage Cards as a standardized way to report AI assistance across scientific work and domains. It describes potential uses for monitoring and trend analysis, while noting limits in who can report for a project.

6 DISCUSSION

  • AI Usage Cards provide a standardized way to report AI use across steps of scientific work and serve as a framework across domains.The paper also describes a free service that creates cards through an interactive questionnaire.Source: §6 · p. 5
  • Machine-readable cards can support AI-use monitoring and help policymakers evaluate decisions.The paper compares its standardized, transferable reporting approach with other efforts.Source: §6 · p. 5
  • Open acknowledgment lets communities consider whether using particular models during project phases is reasonable when biases exist.Analyzing cards across a field or area can help reveal trends in AI usage.Source: §6 · p. 5
  • The current card version supports only a limited number of reporters, so it cannot account for many individuals in a scientific project.The authors suggest focal points or group leaders take responsibility for reporting their teams’ actions, and call for periodic updates to the framework.Source: §6 · p. 5

A ETHICAL CONSIDERATIONS

AI-generated content can reproduce issues present in human-produced training data, including discrimination and bias. The authors caution that cards do not guarantee safe outputs and that unreported AI use can propagate harmful content.

A ETHICAL CONSIDERATIONS

  • Because language models are trained mostly on human-produced data, their outputs may reflect issues such as discrimination and bias.The paper mentions diverse training data and discarding wrongful suggestions as possible mitigation approaches.Source: A ETHICAL CONSIDERATIONS · p. 6
  • AI Usage Cards cannot guarantee that AI use serves everyone’s interests or prevents false claims about sensitive topics and individuals.The paper says human evaluation of altered content and ideas remains fundamental.Source: A ETHICAL CONSIDERATIONS · p. 6
  • When researchers do not report their AI use, the card process can propagate bias, unethical statements, and prejudice.The authors characterize the process as not error-free and call for honesty from all parties involved.Source: A ETHICAL CONSIDERATIONS · p. 6

B AI USAGE CARDS FOR THIS PAPER

The authors report using AI for card-name suggestions, theoretical-model method comparisons, and an abstract draft that was not used in the final manuscript.

B AI USAGE CARDS FOR THIS PAPER

C AI USAGE CARDS TEMPLATE

The template gives details for each AI-usage subcategory and includes questions that record which models were used, when they were used, and their versions.

C AI USAGE CARDS TEMPLATE

D DETAILS ON THE QUESTIONNAIRE

The questionnaire collects model and usage information, asks follow-up questions about selected uses and ethics, and then generates and emails completed cards with an inclusion tutorial.

D DETAILS ON THE QUESTIONNAIRE

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