Responsibly strategizing with the metaverse: Business implications and DEI opportunities and challenges

2023 | Journal of Strategic Information Systems | Citations: 0

Authors: Marabelli, Marco; Newell, Sue

Abstract: This viewpoint article aims to lay out the foundations for IS research on the st ... Expand

Abstract: This viewpoint article aims to lay out the foundations for IS research on the strategic implications as well as diversity, equity, and inclusion (DEI) opportunities and challenges of the metaverse – a sociotechnical system providing users with an immersive, synchronous 3D experience of (virtual) reality through interoperable platforms and sensor devices. We first examine the metaverse’s technologies and its underlying business models, which hold immense strategic implications for organizations and industries, and might require rethinking the future of work. As with most emerging technologies able to generate, collect, and process user data, the metaverse comes at a price; leveraging the metaverse strategically will require ethical and DEI considerations. After examining all these aspects of the metaverse, we reflect on ways IS scholarship engaged in strategizing can advance theoretical and practical knowledge by generating novel research questions and outlining a research agenda. Our contribution is twofold: first, we analyze the metaverse using a sociotechnical lens, thereby setting the ground for future studies of the phe­ nomenon. Second, we identify specific strategic implications as well as DEI opportunities and challenges associated with the metaverse, and discuss how scholarship versed in IS strategizing can contribute to addressing them, both theoretically and empirically. Collapse

Topics: virtual reality social media artificial intelligence virtual teamwork outsourcing

Methods: theory development computational algorithm

Theories: socio technical theory

Digital Identity Platforms: A Data Justice Perspective

2023 | HICSS | Citations: 0

Authors: Masiero, Silvia

Abstract: Digital identity platforms, designed to enable secure and unique authentication ... Expand

Abstract: Digital identity platforms, designed to enable secure and unique authentication of users, are widely depicted as a means to strengthen public service systems. Yet, this vision is questioned by studies illuminating how digital authentication results in exclusions, data violations and other forms of harm generated on users. This paper contributes a vision of digital identity inspired by the concept of data justice, which views data in terms of the fairness with which users are seen, represented and treated. Drawing on a data justice framework, we study a dataset of web sources (2021-2022) in terms of the legal, informational and design-related forms of injustice stemming from digital identification. By doing so we contribute to the emerging literature on digital identity, offering a conceptual lens to understand and ultimately combat the injustice generated through it. Collapse

Topics: pandemic blog digital platform data protection information system use

Methods: design artifact theory development

Theories: distributive justice theory

Mitigating Bias in Organizational Development and Use of Artificial Intelligence

2023 | International Conference on Information Systems | Citations: 0

Authors: Tanriverdi, Hüseyin; Akinyemi, John-Patrick; Neumann, Terrence

Abstract: We theorize why some artificial intelligence (AI) algorithms unexpectedly treat ... Expand

Abstract: We theorize why some artificial intelligence (AI) algorithms unexpectedly treat protected classes unfairly. We hypothesize that mechanisms by which AI assumes agencies, rights, and responsibilities of its stakeholders can affect AI bias by increasing complexity and irreducible uncertainties: e.g., AI's learning method, anthropomorphism level, stakeholder utility optimization approach, and acquisition mode (make, buy, collaborate). In a sample of 726 agentic AI, we find that unsupervised and hybrid learning methods increase the likelihood of AI bias, whereas "strict" supervised learning reduces it. Highly anthropomorphic AI increases the likelihood of AI bias. Using AI to optimize one stakeholder's utility increases AI bias risk, whereas jointly optimizing the utilities of multiple stakeholders reduces it. User organizations that co-create AI with developer organizations instead of developing it in-house or acquiring it off-the-shelf reduce AI bias risk. The proposed theory and the findings advance our understanding of responsible development and use of agentic AI. Collapse

Topics: artificial intelligence algorithmic bias Google+ database system algorithmic transparency

Methods: computational algorithm supervised learning survey design optimization model theory development

In the Land of the Blind, the One-Eyed Man Is King: Knowledge Brokerage in the Age of Learning Algorithms

2022 | Organization Science | Citations: 0

Authors: Waardenburg, Lauren; Huysman, Marleen; Sergeeva, Anastasia V.

Abstract: This paper presents research on how knowledge brokers attempt to translate opaqu ... Expand

Abstract: This paper presents research on how knowledge brokers attempt to translate opaque algorithmic predictions. The research is based on a 31-month ethnographic study of the implementation of a learning algorithm by the Dutch police to predict the occurrence of crime incidents and offers one of the first empirical accounts of algorithmic brokers. We studied a group of intelligence officers, who were tasked with brokering between a machine learning community and a user community by translating the outcomes of the learning algorithm to police management. We found that, as knowledge brokers, they performed different translation practices over time and enacted increasingly influential brokerage roles, namely, those of messenger, interpreter, and curator. Triggered by an impassable knowledge boundary yielded by the black-boxed machine learning, the brokers eventually acted like “kings in the land of the blind” and substituted the algorithmic predictions with their own judgments. By emphasizing the dynamic and influential nature of algorithmic brokerage work, we contribute to the literature on knowledge brokerage and translation in the age of learning algorithms. Collapse

Topics: criminality knowledge sharing organization structure predictive policing logistics management

Methods: computational algorithm machine learning field study qualitative interview ethnography

Theories: organizational theory

From coexistence to co-creation: Blurring boundaries in the age of AI

2022 | Information and Organization | Citations: 0

Authors: Waardenburg, Lauren; Huysman, Marleen

Abstract: While the self-learning nature of AI systems that use machine learning calls for ... Expand

Abstract: While the self-learning nature of AI systems that use machine learning calls for sustained cocreation between developers and users during development, implementation and use, informa­ tion systems and management scholars still largely build on a long-established tradition of separating technology development from use. Instead, the self-learning nature of AI calls for letting go of this tradition to separate between development and use, which is starting to happen in practice but has not yet found appropriate theoretical and methodological tools among re­ searchers. In this paper we show some real-life attempts to develop sustained collaboration among developers and users, based on empirical cases of five organizations. In particular, we propose how blurring boundaries makes data production, explainable AI and AI deployment fields of practice where development and use intertwine. We suggest embracing the blurred boundaries of AI implementation in our theorizing, understanding the different parts of AI as fields of practice where development and use come together in the co-creation of AI and work. Collapse

Topics: artificial intelligence participatory design explainable artificial intelligence criminality online learning system

Methods: theory development design methodology computational algorithm

The lifecycle of algorithmic decision-making systems: Organizational choices and ethical challenges

2021 | Journal of Strategic Information Systems | Citations: 0

Authors: Marabelli, Marco; Newell, Sue; Handunge, Valerie

Abstract: In this viewpoint article we discuss algorithmic decision-making systems (ADMS), ... Expand

Abstract: In this viewpoint article we discuss algorithmic decision-making systems (ADMS), which we view as organizational sociotechnical systems with their use in practice having consequences within and beyond organizational boundaries. We build a framework that revolves around the ADMS lifecycle and propose that each phase challenges organizations with “choices” related to technical and processual characteristics – ways to design, implement and use these systems in practice. We argue that it is important that organizations make these strategic choices with awareness and responsibly, as ADMS’ consequences affect a broad array of stakeholders (the workforce, sup­ pliers, customers and society at-large) and involve ethical considerations. With this article we make two main contributions. First, we identify key choices associated with the design, imple­ mentation and use in practice of ADMS in organizations, that build on past literature and are tied to timely industry-related examples. Second, we provide IS scholars with a broad research agenda that will promote the generation of new knowledge and original theorizing within the domain of the strategic applications of ADMS in organizations. Collapse

Topics: privacy decision making predictive policing Uber accounting

Methods: computational algorithm facial recognition system theory development

Theories: socio technical theory

When the Machine Meets the Expert: An Ethnography of Developing AI for Hiring

2021 | Management Information Systems Quarterly | Citations: 39

Authors: van den Broek, Elmira; Sergeeva, Anastasia; Huysman, Marleen

Abstract: The introduction of machine learning (ML) in organizations comes with the claim ... Expand

Abstract: The introduction of machine learning (ML) in organizations comes with the claim that algorithms will produce insights superior to those of experts by discovering the "truth" from data. Such a claim gives rise to a tension between the need to produce knowledge independent of domain experts and the need to remain relevant to the domain the system serves. This two-year ethnographic study focuses on how developers managed this tension when building an ML system to support the process of hiring job candidates at a large international organization. Despite the initial goal of getting domain experts "out the loop," we found that developers and experts arrived at a new hybrid practice that relied on a combination of ML and domain expertise. We explain this outcome as resulting from a process of mutual learning in which deep engagement with the technology triggered actors to reflect on how they produced knowledge. These reflections prompted the developers to iterate between excluding domain expertise from the ML system and including it. Contrary to common views that imply an opposition between ML and domain expertise, our study foregrounds their interdependence and as such shows the dialectic nature of developing ML. We discuss the theoretical implications of these findings for the literature on information technologies and knowledge work, information system development and implementation, and human-ML hybrids.How ML improves the creation, sharing, and use of knowledge in organizations is different from how traditional information technologies (IT) attempted to do so. These knowledge systems, such as intranets, expert systems, and virtual networks, were mainly introduced to augment knowledge work and depended on practitioners contributing to them Collapse

Topics: knowledge work systems development research and development spreadsheet personality

Methods: qualitative interview computational algorithm field study company material ethnography

THE AFFORDANCE-ACTUALIZATION PROCESS IN A PREDICTIVE POLICING CONTEXT: INSIGHTS FROM THE FRENCH MILITARY POLICE

2020 | European Conference On Information Systems | Citations: 0

Authors: Cécile, Godé; Sébastien, Brion; Amélie, Bohas

Abstract: Drawing on the Affordance-Actualization Theory (AAT), our article addresses the ... Expand

Abstract: Drawing on the Affordance-Actualization Theory (AAT), our article addresses the following research question: how do the affordances of a predictive analytics are actualized? We aim at providing a conceptual framework of the affordance-actualization process which works to advance our understanding of the way actors and a predictive system interact to actualize affordances. We employed an exploratory multisite case study focusing on a predictive policing system recently implemented in French military police -Gendarmerie. Based on the qualitative content analysis of datasets gathered over an 18-month, we show that the actualization process of affordance rests on different patterns of uses developed by police officers in interaction with associated outcomes. We further highlight how these patterns-of-usesoutcomes interactions result in non, partial or full actualization of perceived affordances. Collapse

Topics: affordance usability user experience predictive policing criminality

Methods: machine learning case study qualitative interview archival research personal interview

Theories: theory of affordance

Information systems research on artificial intelligence and work: A commentary on “Robo-Apocalypse cancelled? Reframing the automation and future of work debate”

2020 | Journal of Information Technology | Citations: 0

Authors: Huysman, Marleen

Abstract: ... Expand

Abstract: Collapse

Topics: artificial intelligence predictive policing

Preserving Validity of Predictive Services over Time

2019 | HICSS | Citations: 0

Authors: Baier, Lucas

Abstract: Companies more and more rely on predictive services which are constantly monitor ... Expand

Abstract: Companies more and more rely on predictive services which are constantly monitoring and analyzing the available data streams for better service offerings. However, sudden or incremental changes in those streams are a challenge for the validity and proper functionality of the predictive service over time. We develop a framework which allows to characterize and differentiate predictive services with regard to their ongoing validity. Furthermore, this work proposes a research agenda of worthwhile research topics to improve the long-term validity of predictive services. In our work, we especially focus on different scenarios of true label availability for predictive services as well as the integration of expert knowledge. With these insights at hand, we lay an important foundation for future research in the field of valid predictive services. Collapse

Topics: predictive policing

Methods: literature study statistical hypothesis test qualitative interview supervised learning machine learning