It is important to note that the text of this editorial is entirely written by humans without any Generative Artificial Intelligence (GAI) contribution or assistance. The Editor of the ISJ (Robert M. Davison) was contacted by one of the ISJ's Associate Editors (AE) (Marjolein van Offenbeek) who explained that the qualitative data analysis software ATLAS.ti was offering a free-of-charge analysis of research data if the researcher shared the same data with ATLAS.ti for training purposes for their GAI1 analysis tool. Marjolein believed that this spawned an ethical dilemma. Robert forwarded Marjolein's email to the ISJ's Senior Editors (SEs) and Associate Editors (AEs) and invited their comments. Nine of the SEs and AEs replied with feedback. We (the 11 contributing authors) then engaged in a couple of rounds of brainstorming before amalgamating the text in a shared document. This was initially created by Hameed Chughtai, but then commented on and edited by all the members of the team. The final version constitutes the shared opinion of the 11 members of the team, after several rounds of discussion. It is important to emphasise that the 11 authors have contrasting views about whether GAI should be used in qualitative data analysis, but we have reached broad agreement about the ethical issues associated with this use of GAI. Although many other topics related to the use of GAI in research could be discussed, for example, how GAI could be effectively used for qualitative analysis, we believe that ethical concerns overarch many of these other topics. Thus, in this editorial we exclusively focus on the ethics associated with using GAI for qualitative data analysis. The emergence and ready availability of GAI has profound implications for research. This powerful technology, capable of generating human-like text, has the potential to create many opportunities for researchers in all disciplines. However, the technology brings ethical challenges and risks. We unearth and comment on many facets of qualitative data-related ethics. Our goal is to engage with and inform the many stakeholders of the ISJ, including other editors, (prospective) authors, reviewers and readers. We intend that this discussion serves as a starting point for a broader conversation on how we can responsibly navigate the evolving landscape of GAI in research. It is important to point out that we are not advocating for or against the use of GAI in research, nor are we attempting to find ways to make it easier (or harder) for researchers to incorporate GAI in their research designs and practices. Our focus relates to the ethical issues associated with GAI use in analysing qualitative data that scholars, in the conduct of their academic research, may encounter and should consider. One of the allures of GAI lies in its capability to discover patterns to produce new codes in a data corpus faster and more comprehensively than humans, by drawing from its trained data. This capability implies that GAI may identify patterns missed by humans. However, speed and comprehensiveness do not necessarily translate to appropriateness, substantive helpfulness or insightful understanding. More fundamentally, speed and comprehensiveness should not be achieved at the cost of unethical research practices or of the commitment to ‘do no harm’ to individuals, communities, organisations and society from research participation (Iphofen (2) data privacy and transparency; (3) interpretive sufficiency; (4) biases manifested in GAI, and (5) researcher responsibilities and agency. We foresee that this exploration would eventually enable us to inform the development of living guidelines for qualitative data analysis, pertaining to ISJ and the Information Systems field more generally, in the context of GAI. Our hope is that such living guidelines will align with broader discussions in scholarship and emerging AI policies around the world2,3. In addition, this editorial reacts to GAI-related policies that other journals have already set, for instance, the recent Academy of Management Review's editorial (Grimes et al., 2023), which are indicators of the siloed approach that not just academic fields but also individual journals are taking, for making sense of the use of GAI in scholarly settings. We instead aim at developing a fluid document that simply points to potential ethical implications of using GAI, and we do so by focusing specifically on qualitative data analysis. We are concerned about surrendering research data to commercial entities, for example, sharing data with a GAI tool in exchange for automated analysis because that could violate data rights and confidentiality. While automated data analysis is standard for quantitative data, using Language Learning Models such as ChatGPT for qualitative data is different as they require data for training their models. Qualitative research is typically an in-depth inquiry that uses ‘relatively unstructured forms of data, whether produced through observation, interviewing and/or the analysis of documents’ (Hammersley for instance, with respect to critical interpretive studies, an automated coding process could lead to a banal and neutral analysis that fails to identify or disclose hidden aspects in the qualitative data. The output analysis will then incorporate an incomplete and potentially superficial reading of the data. Further, mainstream (or neutral) chunking and coding could influence and limit our potential learning from the data analysis. For instance, it is important that the researcher be aware of the risks associated with introducing and reinforcing existing biases, especially in research on marginalisation, oppression, activism, conflicts and decolonisation. In addition, in an investigation of socially relevant problems stemming from digital technology, the researcher bounded by personal and community responsibility joins forces with the researched to produce understanding that empowers those disadvantaged by the technology (Amis accountability remains with the researcher (Gregor, 2024). GAI cannot be listed as a co-author (this is publisher policy at ISJ), and thus cannot be permitted to have any agency in the research or its outputs. To wit, we consider blind, automated applications of GAI for data analysis without human agency unethical in every aspect of the research process. We acknowledge that some ethical issues are specific to particular GAI implementations, which change over time, emphasising the need for clear quality criteria. GAI implementations could also be private. For example, some research organisations have established their own GAI service, enabling students and researchers to use OpenAI's GPT models within university and national data privacy requirements.8 In this view, the ethical concerns, such as privacy, are specific to implementations of GAI and are not necessarily general issues with the technology class. However, private Language Learning Models are not necessarily expected to improve the quality of coding; they might still be too generic to address specific research questions. While they can, to a certain extent, address privacy issues, they cannot unequivocally improve analysis quality and their biases may still be present. Artificial Intelligence Generated Content (AIGC) tools, such as ChatGPT and others based on large language models (LLMs), cannot be considered capable of initiating an original piece of research without direction by human authors. They also cannot be accountable for a published work or for research design, which is a generally held requirement of authorship (as discussed in the previous section), nor do they have legal standing or the ability to hold or assign copyright. Therefore, in accordance with COPE's position statement on AI tools,11 these tools cannot fulfil the role of, nor be listed as, an author of an article. If an author has used this kind of tool to develop any portion of a manuscript, its use must be described, transparently and in detail, in the Methods or Acknowledgements section. The author is fully responsible for the accuracy of any information provided by the tool and for any work on which that information that are used to improve and general are not included in the of these The final about whether use of an tool is or in the of a or a published lies with the or other responsible for the editorial our analysis and given all these characteristics of GAI, we that researchers should engage in critical and to and address the ethical issues the use of GAI in their research practices qualitative data analysis. We do not to a where we are into that GAI use is and that researchers do not need to particular to or to their use of Robert M. Hameed Chughtai, Marjolein van and to this
Davison et al. (Sun,) studied this question.