Ethics in Information Systems and Design Science Research: Five Perspectives

Herwix, Alexander; Haj-Bolouri, Amir; Rossi, Matti; Tremblay, Monica Chiarini; Purao, Sandeep; Gregor, Shirley

doi:10.17705/1cais.05028

Cited by 10 publications

(10 citation statements)

References 71 publications

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“…WALL-E underscores the importance of empathetic engagement, for without empathy, WALL-E is not perceived as real (Srivastava, 2016). Herwix et al (2022) observed a relationship between perceived technical competence and both affect-based and cognitive-based trust, reinforcing the importance of empathy in human computer interaction (HCI) when planning digital health interventions (Søgaard Neilsen and Wilson, 2019). Just as empathetic engagement occurs for the viewer, so too, it is important that clinicians, researchers and developers recognise the empathetic components necessary to convey holistic trust in AI within the clinical setting.…”

Section: Empathymentioning

confidence: 86%

Artificial Intelligence in nursing: trustworthy or reliable?

Higgins,

Chalup,

Wilson

2024

Journal of Research in Nursing

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Background: Trustworthiness in Artificial Intelligence (AI) innovation is a priority for governments, researchers and clinicians; however, clinicians have highlighted trust and confidence as barriers to their acceptance of AI within a clinical application. While there is a call to design and develop AI that is considered trustworthy, AI still lacks the emotional capability to facilitate the reciprocal nature of trust. Aim: This paper aims to highlight and discuss the enigma of seeking or expecting trust attributes from a machine and, secondly, reframe the interpretation of trustworthiness for AI through evaluating its reliability and validity as consistent with the use of other clinical instruments. Results: AI interventions should be described in terms of competence, reliability and validity as expected of other clinical tools where quality and safety are a priority. Nurses should be presented with treatment recommendations that describe the validity and confidence of prediction with the final decision for care made by nurses. Future research should be framed to better understand how AI is used to deliver care. Finally, there is a responsibility for developers and researchers to influence the conversation about AI and its power towards improving outcomes. Conclusion: The sole focus on demonstrating trust rather than the business-as-usual requirement for reliability and validity attributes during implementation phases may result in negative experiences for nurses and clinical users. Implications for practice: This research will have significant implications for the way in which future nursing is practised. As AI-based systems become a part of routine practice, nurses will be faced with an increasing number of interventions that require complex trust systems to operate. For any AI researchers and developers, understanding the complexity of trust and creditability in the use of AI in nursing will be crucial for successful implementation. This research will contribute and assist in understanding nurses’ role in this change.

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Section: Empathymentioning

confidence: 86%

Artificial Intelligence in nursing: trustworthy or reliable?

Higgins,

Chalup,

Wilson

2024

Journal of Research in Nursing

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“…In addition, practical applications of the actionable insights in this paper, in a form of strategic and operational frameworks or case studies, can be another pragmatic endeavor by practitioners and researchers. Herwix et al (2022) highlighted the importance of more serious and systematic engagement with the selection, framing and prioritization of ethical issues. There is an emphasis among the state-of-the-art for the need to be more aware, anticipatory, reflecting and informed about the variety of perspectives and contemporary debates concerning AIED ethics.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Today’s Education Landscape: Understanding and Managing Ethical Issues for Educational Assessment

Lim

Gottipati

Cheong

2023

Preprint

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In a societal institution as fundamental as education, when teaching practitioners or researchers apply artificial intelligence in academic processes such as assessments, it is important to study the divide between what may be ethically permissible and not permissible. This study applied a systematic literature mapping methodology to scour extant research, so as to holistically structure the landscape into explicit topical research clusters. Through topic modelling and network analyses, research mapped ten key ethical principles to five research archetypical domains, and reviewed the contribution and intensity of these ethical principles in each thematic domain. The study extended this review, by mapping out ethics programs and activities that can be applied in practice, alongside their relevant underpinning theories. This study provides a comprehensive treatment of this subject matter to date. We hope the findings of this research can provide researchers and practitioners the insights into the application methods of AI in assessments, and in particular, in terms of their intertwined ethical challenges and how these challenges may be addressed, for follow up studies.

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“…First, broadening the scope of DSR from individual research projects to a "super-methodology" for coordinating an entire research ecosystem is a promising step to meet the growing calls for IS research to more systematically address the grand challenges facing our societies (e.g., Boh et al, 2023;Davison et al, 2023;Desouza & Dawson, 2023;Gable, 2020;Gholami et al, 2016;Herwix et al, 2022;Majchrzak et al, 2016;Winter & Butler, 2011). This is demonstrated by several large-scale research efforts that have addressed real-world problems and used DSR frameworks to coordinate and integrate their work (e.g., Ginige et al, 2018;Nunamaker et al, 2013Nunamaker et al, , 2017Paradice et al, 2019).…”

Section: Background On Responsible Design Science Research From Desig...mentioning

confidence: 99%

“…This is often the case when problem framings or proposed solutions become highly contested (Wanzenböck et al, 2020). Due to the interdisciplinary and transformative nature of responsible DSR and its focus on wicked problems, we may encounter such conflicts at all stages of inquiry (Herwix et al, 2022). For example, a recurring theme in the interviews was that researchers face competing interests between working to publish contributions to academic discourse and helping practitioners and societal stakeholders improve their own conditions:…”

Section: Managing Conflicting Values and Positionsmentioning

confidence: 99%

“…On the one hand, I build on and extend DSR because it explicitly recognizes normativity, transformation, and real-world impact as core dimensions of IS research (e.g., Benke et al, 2020;Herwix et al, 2022;Myers & Venable, 2014), whereas BSR tends to do so less explicitly, often claiming a neutral stance with respect to its own relationship to the world (Douglas, 2009;Hevner et al, 2004). In this sense, DSR is the more appropriate starting point for developing a research ecosystem dedicated to implementing and advancing RI.…”

Section: Introductionmentioning

confidence: 99%

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Threading the Needle in the Digital Age: Four Paradigmatic Challenges for Responsible Design Science Research

Herwix

2023

Preprint

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The rapid development of life-changing information technologies poses a significant societal challenge: finding responsible ways to harness their benefits while mitigating potentially catastrophic consequences. However, our understanding of how to contribute to this imperative is still in its early stages, especially in the face of accelerating technological advances, such as in the field of artificial intelligence. In response, this paper proposes the deliberate development of a responsible design science research (DSR) ecosystem as a promising way forward. This responsible DSR ecosystem aims to leverage the insights and diversity of the information systems (IS) research community, along with perspectives from related fields, to address societal challenges in a responsible and productive manner. The paper presents a conceptualization of responsible DSR, characterized by four paradigmatic challenges: ontological, epistemological, axiological, and methodological. Through a multi-grounded research process involving empirical, theoretical, and internal grounding, key challenges for responsible DSR practice are conceptualized and recommendations for its future development are provided. This paper contributes to IS research by presenting a paradigmatic framework that can provide a common ground for the wide variety of IS research interested in addressing real-world problems, and by offering recommendations that open up new perspectives on the possibilities and limitations of responsible DSR. Overall, this work can be seen as nothing more or less than an invitation to embark on a collective journey to imagine a responsible DSR ecosystem and shape its future for the benefit of humanity.

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Ethics in Information Systems and Design Science Research: Five Perspectives

Cited by 10 publications

References 71 publications

Artificial Intelligence in nursing: trustworthy or reliable?

Artificial Intelligence in nursing: trustworthy or reliable?

Artificial Intelligence in Today’s Education Landscape: Understanding and Managing Ethical Issues for Educational Assessment

Threading the Needle in the Digital Age: Four Paradigmatic Challenges for Responsible Design Science Research

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