Disclosing Generative AI Use in Digital Humanities Research

• URL: http://arxiv.org/abs/2507.03216v1
• Date: Thu, 03 Jul 2025 23:11:45 GMT
• Title: Disclosing Generative AI Use in Digital Humanities Research
• Authors: Rongqian Ma, Xuhan Zhang, Adrian Wisnicki,
• Abstract summary: This survey study investigates how digital humanists perceive and approach generative AI disclosure in research.<n>Results indicate that while digital humanities scholars acknowledge the importance of disclosing GenAI use, the actual rate of disclosure in research practice remains low.
• Score: 0.6144680854063939
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This survey study investigates how digital humanists perceive and approach generative AI disclosure in research. The results indicate that while digital humanities scholars acknowledge the importance of disclosing GenAI use, the actual rate of disclosure in research practice remains low. Respondents differ in their views on which activities most require disclosure and on the most appropriate methods for doing so. Most also believe that safeguards for AI disclosure should be established through institutional policies rather than left to individual decisions. The study's findings will offer empirical guidance to scholars, institutional leaders, funders, and other stakeholders responsible for shaping effective disclosure policies.

Related papers

• Identity Theft in AI Conference Peer Review [50.18240135317708]
  We discuss newly uncovered cases of identity theft in the scientific peer-review process within artificial intelligence (AI) research.<n>We detail how dishonest researchers exploit the peer-review system by creating fraudulent reviewer profiles to manipulate paper evaluations.
  arXiv  Detail & Related papers  (2025-08-06T02:36:52Z)
• Social Scientists on the Role of AI in Research [2.2665233748698355]
  We present a community-centric study drawing on 284 survey responses and 15 semi-structured interviews with social scientists.<n>We find that the use of AI in research settings has increased significantly among social scientists in step with the widespread popularity of generative AI (genAI)<n>Ethical concerns, particularly around automation bias, deskilling, research misconduct, complex interpretability, and representational harm, are raised in relation to genAI.
  arXiv  Detail & Related papers  (2025-06-12T19:55:36Z)
• Generative Artificial Intelligence for Academic Research: Evidence from Guidance Issued for Researchers by Higher Education Institutions in the United States [4.06279597585806]
  generative AI (GenAI) has signaled a significant shift in research activities.<n>This has raised questions about how to balance the seemingly productive uses of GenAI with ethical concerns.<n>To address these concerns, many Higher Education Institutions (HEIs) have released institutional guidance for researchers.
  arXiv  Detail & Related papers  (2025-03-01T23:34:02Z)
• Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation [58.064940977804596]
  A plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently.<n>Ethical concerns regarding shortcomings of these tools and potential for misuse take a particularly prominent place in our discussion.
  arXiv  Detail & Related papers  (2025-02-07T18:26:45Z)
• Research Integrity and GenAI: A Systematic Analysis of Ethical Challenges Across Research Phases [0.0]
  The rapid development and use of generative AI (GenAI) tools in academia presents complex and multifaceted ethical challenges for its users.<n>This study aims to examine the ethical concerns arising from the use of GenAI across different phases of research.
  arXiv  Detail & Related papers  (2024-12-13T13:31:45Z)
• Privacy Risks of General-Purpose AI Systems: A Foundation for Investigating Practitioner Perspectives [47.17703009473386]
  Powerful AI models have led to impressive leaps in performance across a wide range of tasks. Privacy concerns have led to a wealth of literature covering various privacy risks and vulnerabilities of AI models. We conduct a systematic review of these survey papers to provide a concise and usable overview of privacy risks in GPAIS.
  arXiv  Detail & Related papers  (2024-07-02T07:49:48Z)
• A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures [50.987594546912725]
  Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures.
  arXiv  Detail & Related papers  (2024-03-31T12:44:48Z)
• The ethical ambiguity of AI data enrichment: Measuring gaps in research ethics norms and practices [2.28438857884398]
  This study explores how, and to what extent, comparable research ethics requirements and norms have developed for AI research and data enrichment. Leading AI venues have begun to establish protocols for human data collection, but these are are inconsistently followed by authors.
  arXiv  Detail & Related papers  (2023-06-01T16:12:55Z)
• Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment [66.91538273487379]
  There is a certain consensus about the need to develop AI applications with a Human-Centric approach. Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes. We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
  arXiv  Detail & Related papers  (2023-02-13T16:44:44Z)
• Metaethical Perspectives on 'Benchmarking' AI Ethics [81.65697003067841]
  Benchmarks are seen as the cornerstone for measuring technical progress in Artificial Intelligence (AI) research. An increasingly prominent research area in AI is ethics, which currently has no set of benchmarks nor commonly accepted way for measuring the 'ethicality' of an AI system. We argue that it makes more sense to talk about 'values' rather than 'ethics' when considering the possible actions of present and future AI systems.
  arXiv  Detail & Related papers  (2022-04-11T14:36:39Z)
• Individual Explanations in Machine Learning Models: A Survey for Practitioners [69.02688684221265]
  The use of sophisticated statistical models that influence decisions in domains of high societal relevance is on the rise. Many governments, institutions, and companies are reluctant to their adoption as their output is often difficult to explain in human-interpretable ways. Recently, the academic literature has proposed a substantial amount of methods for providing interpretable explanations to machine learning models.
  arXiv  Detail & Related papers  (2021-04-09T01:46:34Z)
• A narrowing of AI research? [0.0]
  We study the evolution of the thematic diversity of AI research in academia and the private sector. We measure the influence of private companies in AI research through the citations they receive and their collaborations with other institutions.
  arXiv  Detail & Related papers  (2020-09-22T08:23:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.