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This paper highlights the expertise of marginalised and underserved populations, placing them at thecentre of understanding resilient information systems design. We present a collaborative project inwhich women experiencing homelessness, alongside researchers in information systems and healthcaresciences, co-produce digital health interventions aimed at addressing psychological distress and safetyconcerns. Homelessness is associated with exclusion, resource deprivation, trauma, and abuse. Bylearning from these deeply challenging lived experiences, marginalised and underserved communitiesoffer invaluable insights into how information systems should be designed to support users duringepisodes of violence, trauma, and crisis. Key improvements include system covertness, robustness,responsiveness, timely activation, and personalised privacy settings. This study further provides criticalinsights into the value of collaborative, co-production-focused design research, illuminating therequirements of resilient information systems that serve a broader user base.

This article discusses the key existential stakes of implementing biometrics in human lifeworlds. In this pursuit, we offer a problematization and reinvention of central values often taken for granted within the “ethical turn” of AI development and discourse, such as autonomy, agency, privacy and integrity, as we revisit basic questions about what it means to be human and embodied. Within a framework of existential media studies, we introduce an existential ethics of care—through a conversation between existentialism, virtue ethics, a feminist ethics of care and post-humanist ethics—aiming to deepen and nuance our understanding of the human behind “human-centered” AI directives. The key argument is that biometrics implicates humans through unprecedented forms of objectification, through which the existential body—the relational, intimate and frail human being—is at risk. We interrogate these risks as they become visible at three sites where embodied humans are challenged by biometrics, and thus where the existential body is challenged by the biometric body. This occurs through reductionism (biometric passports nailing bodies to identities, removing human judgment and compromising agency at the AI border), enforced transparency (smart home assistants surveying human intimacies and invading intimate spaces in the bedroom) and the breaching of bodily integrity (chipping bodies to capture sensory data, challenging the very concept of bodily integrity through self-invasive biohacking). Our existential ethics of care is importantly not a solutionist list of principles or suggestions, but a manifesto for a way of thinking about the ethical challenges of living with biometrics in today’s world, by raising the right questions. We argue that a revitalized discussion of the basic existential stakes within human lived experience is needed and should serve as the foundation on which comprehensive frameworks can be built to address the complexities and prospects for ethical machines, responsible biometrics and AI.

The main protagonist in Kazuo Ishiguro's latest novel is Klara, an artificial friend whose existential goal is to be a child's companion. Some aspects of this fictional narrative have begun to gradually enter our daily lives. Products reminiscent of Klara are available abundantly on the market: smart toys, adaptive learning applications, and companion robots. Children can relate to these products and perform activities together with them. Preliminary research has shown fundamental differences between existing technologies and these emerging children's digital companions. However, we still do not know much about their benefits and risks. This paper explores different and even contradicting perspectives on the phenomenon. We present the discussion from four perspectives -1) temporality, 2) use, 3) trust and ethics, and 4) sociotechnical design -and conclude the paper with an agenda for interdisciplinary IS research. The agenda points to the need for a psychological, medical, engineering, and temporal research community to understand this emerging sociotechnical phenomenon and design its future for the better.

Many would argue that artificial intelligence (AI) is not only technology but also a paradigmatic shift in the relationship between humans and machines. Much literature assumes that AI-powered practices substantially differ from and profoundly change organizational structures, communication, affordances, and ecosystems. However, AI research remains fragmented and often lacks clarity. While the information systems (IS) discipline can play a pivotal role in AI’s emergence and use, the discipline needs a clear direction that specifies how it can contribute and its key research themes and questions. This paper draws on a professional development workshop that we organized at the 2020 International Conference on Information Systems and the discussions that followed. We summarize and synthesize how AI has impacted organizational practices over five decades and provide views from various perspectives. We identify weaknesses in the current AI literature as measured against conceptual clarity, theoretical glue, cumulative tradition, parsimony, and applicability. We also identify direct actions that the IS research community can undertake to address these issues. Finally, we propose a next-step research agenda to guide AI research in the coming years.

Artificial Intelligence (AI) has been argued to offer a myriad of improvements in how we work and live. The notion of AI comprises a wide-ranging set of technologies that allow individuals and organizations to integrate and analyze data and use that insight to improve or automate decision-making. While most attention has been placed on the positive aspects companies realize by the adoption by the adoption and use of AI, there is a growing concern around the negative and unintended consequences of such technologies. In this special issue we have made a call for research papers that help us explore the dark side of AI use. By adopting a dark side lens, we aimed to expand our understanding of how AI should be implemented in practice, and how to minimize or avoid negative outcomes. In this editorial, we build on the notion of responsible AI, to highlight the different ways in which AI can potentially produce unintended consequences, as well as to suggest alternative paths future IS research can follow to improve our knowledge about how to mitigate such occurrences. We further expand on dark side theorizing in order to uncover hidden assumptions of current literature as well as to propose other prominent themes that can guide future IS research on AI adoption and use.

The recently proposed theory of data network effects aims to account for how user value is created from the use of machine learning technology. The theory recognises the unique learning ability of machine learning, which uses large data sets for predictions and decision making. This paper offers a critical assessment of that theory, unearthing some of its strengths and limitations. The latter are transformed into a set of interrelated research questions that jointly constitute the proposed research programme into how to use machine learning technology in order to create and capture value. The paper contributes to the literature with an articulation of some novel knowledge gaps.

A firm’s business model accounts for direct and indirect network effects, where the network size is a key enabler of value creation and appropriation. Additional conception of a business network’s contribution is provided by a recent advancement of the theory of data network effects, where machine learning is used to analyze large data sets to learn, predict, and improve. The more learning there is, the more value is generated, producing ever more data and learning and creating a virtuous circle. For the first time, this study combines the theory of data network effects with business model theory. The contribution lies in extending a business model’s lock-in effects through direct and indirect network effects to encompass data network effects. This paper provides a case study that supports the theoretical advancement and illustrates how this form of machine learning can increase profitability while reducing negative ecological impacts in an industrial context.

This article sets out to explore the phenomenon of willing digital disconnect by reconsidering and reworking some of the central ideas that currently fall under the umbrella of technological non-use. The presupposition of binary divisions between the dichotomies 'users'-'non-users' and 'analogue'-'digital' is put into question as the article explores the taking up of predigital technologies and the explicit and implicit disengagement from contemporary digital technologies. In short, this article asks: What does the contemporary revival of analogue technologies reveal about the social and material processes that constitute 'use', and what are the implications for the conceptual division of the terms analogue and digital? To answer these questions, the article draws on assemblage theory to describe the material and expressive performativity of social structure - that is, how individuals interact with technology. Empirical evidence comes from three illustrative cases where predigital technologies have replaced an existing digital alternative. Results emphasize the importance of understanding the material and expressive reconfigurations that underline technological use in a post-digital society in order to move beyond binary concepts such as analogue/digital or use/non-use as well as concepts such as the digital divide.