How AI-Based Systems Can Induce Reflections: The Case of AI-Augmented Diagnostic Work

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Abdel-Karim, Benjamin; Pfeuffer, Nicolas; Carl, K. Valerie; Hinz, Oliver

Abstract: This paper addresses a thus-far neglected dimension in human-artificial intelli ... Expand

Abstract: This paper addresses a thus-far neglected dimension in human-artificial intelligence (AI) augmentation: machine-induced reflections. By establishing a grounded theoretical-informed model of machine-induced reflection, we contribute to the ongoing discussion in information systems (IS) regarding AI and research on reflection theories. In our multistage study, physicians used a machine learning-based (ML) clinical decision support system (CDSS) to see if and how this interaction can stimulate reflective practice in the context of an X-ray diagnosis task. By analyzing verbal protocols, performance metrics, and survey data, we developed an integrative theoretical foundation to explain how ML-based systems can help stimulate reflective practice. Individuals engage in more critical or shallower modes depending on whether they perceive a conflict or agreement with these CDSS systems, which in turn leads to different levels of reflection depth. By uncovering the process of machine-induced reflections, we offer IS research a different perspective on how such AI-based systems can help individuals become more reflective, and consequently more effective, professionals. This perspective stands in stark contrast to the traditional, efficiency-focused view of MLbased decision support systems and also enriches theories on human-AI augmentation. Collapse

Topics: artificial intelligence decision making decision support system algorithmic transparency recommender system

Methods: cognitive walkthrough survey theory development qualitative interview Student's t-test

Theories: learning theory information systems theory theory of abduction

Explaining Explainable Artificial Intelligence: An integrative model of objective and subjective influences on XAI

2023 | HICSS | Citations: 0

Authors: Alarcon, G.; Willis, S.

Abstract: Explainable artificial intelligence (XAI) is a new field within artificial inte ... Expand

Abstract: Explainable artificial intelligence (XAI) is a new field within artificial intelligence (AI) and machine learning (ML). XAI offers transparency of AI and ML that can bridge the gap in information that has been absent from "black-box" models. Given its nascency, there are several taxonomies of XAI in the literature. The current paper incorporates the taxonomies in the literature into one unifying framework, which defines the types of explanations, types of transparency, and model methods that together inform the user's processes towards developing trust in AI and ML systems. Collapse

Topics: artificial intelligence explainable artificial intelligence usability human computer interaction

Methods: computational algorithm theory development artificial neural network

Accepting the Familiar: The Effect of Perceived Similarity with AI Agents on Intention to Use and the Mediating Effect of IT Identity

2023 | HICSS | Citations: 0

Authors: Alawi, N.; De Vreede, T.; De Vreede, G.

Abstract: With the rise and integration of AI technologies within organizations, our unde ... Expand

Abstract: With the rise and integration of AI technologies within organizations, our understanding of the impact of this technology on individuals remains limited. Although the IS use literature provides important guidance for organization to increase employees' willingness to work with new technology, the utilitarian view of prior IS use research limits its application considering the new evolving social interaction between humans and AI agents. We contribute to the IS use literature by implementing a social view to understand the impact of AI agents on an individual's perception and behavior. By focusing on the main design dimensions of AI agents, we propose a framework that utilizes social psychology theories to explain the impact of those design dimensions on individuals. Specifically, we build on Similarity Attraction Theory to propose an AI similarity-continuance model that aims to explain how similarity with AI agents influence individuals' IT identity and intention to continue working with it. Through an online brainstorming experiment, we found that similarity with AI agents indeed has a positive impact on IT identity and on the intention to continue working with the AI agent. Collapse

Topics: artificial intelligence usage intention behavioral intention brainstorming usability

Methods: chatbot experiment survey descriptive statistic statistical hypothesis test

Theories: social psychology theory

D&L: A Natural Language Processing Based Approach for Protecting Sensitive Information from Shoulder Surfing Attacks

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Aldossari, Marran; Zhang, Dongsong

Abstract: Despite the increasing attention and research effort, how to protect sensitive ... Expand

Abstract: Despite the increasing attention and research effort, how to protect sensitive information from shoulder surfing attacks is still under studied. Existing methods for protecting sensitive textual content on users' screens from shoulder surfing attacks have various limitations, including ineffectiveness, insufficient protection of sensitive information, low usability, and high cognitive workload. To address those limitations, this paper proposes, develops, and evaluates a new solution called "detection and labeling" (D&L), which uses NLP techniques to automatically detect and label sensitive information in the textual content. The labeled and hidden sensitive information is then read to users through their headphones upon their clicking a label. Evaluation results demonstrate that D&L improves protection, enhances usability, reduces users' cognitive workload, and allows faster browsing speed compared to the baseline methods. Collapse

Topics: electronic mail laptop computer usability privacy perceived ease of use

Methods: survey parametric test experiment Tukey HSD for post poc analysis ANOVA

“Collaborating” with AI: Taking a System View to Explore the Future of Work

2023 | Organization Science | Citations: 0

Authors: Anthony, Callen; Bechky, Beth A.; Fayard, Anne-Laure

Abstract: In the wake of media hype about artificial intelligence (AI)/human collaboration ... Expand

Abstract: In the wake of media hype about artificial intelligence (AI)/human collaboration, organizations are investing considerable resources into developing and using AI. In this paper, we draw on theories of technology in organizations to frame new directions for the study of what it means to work “with” AI. Drawing on prior literature, we consider how interactions between users and AI might unfold through theoretical lenses which cast technology as a tool and as a medium. Reflecting on how AI technologies diverge from technologies studied in the past, we propose a new perspective, which considers technology as a counterpart in a system of work that includes its design, implementation, and use. This perspective encourages developing a grounded understanding of how AI intersects with work, and therefore ethnography, building on thick descriptions, is an apt approach. We argue that relational ethnographic approaches can assist organization theorists in navigating the methodological challenges of taking a counterpart perspective and propose several strategies for future research. Collapse

Topics: artificial intelligence spreadsheet human AI collaboration human computer interaction information system use

Methods: ethnography computational algorithm qualitative interview experiment theory development

Theories: theory of distributed cognition actor network theory

Information systems for sustainable remote workplaces

2023 | Journal of Strategic Information Systems | Citations: 0

Authors: Asatiani, Aleksandre; Norström, Livia

Abstract: This review discusses the challenges associated with the sustainability of remot ... Expand

Abstract: This review discusses the challenges associated with the sustainability of remote workplaces, which have become more prevalent due to the growing trend of work digitalization and the pandemic-induced push to remote work. These challenges are highlighted in literature across various disciplines, including information systems, but these discourses have remained isolated from each other. In this review, we consolidated and synthesized research on remote work from the perspective of individual workers by reviewing 187 articles published between 1999 and 2020 in recognized academic journals from fields including information systems, organizational studies, economics, human resources, sociology, and psychology. We identified five key themes that concern opportunities and challenges to sustainable remote workplaces: (1) key character­ istics, (2) work-life boundaries; (3) health and well-being; (4) social interaction, and (5) lead­ ership. Building on our findings we created a framework that recognizes two interrelated categories of factors influencing remote workplace sustainability – rigid base characteristics and contextual remote workplace variables – that together shape the trajectory of remote workplace sustainability in the long term. The framework also identifies the potential role of information systems in modulating the impact of the base characteristics to build continuities that encourage more sustainable remote workplaces. The paper concludes by offering a research agenda for in­ formation systems for sustainable remote workplaces based on the three IS theoretical frames: inclusion, dignity, and boundary objects. Collapse

Topics: remote work IT supported collaboration green IS pandemic task characteristic

Methods: literature sample literature study laboratory experiment journal ranking mixed method

Theories: boundary objects theory leader–member exchange theory

Dynamics of Trust: Unpacking Trust in Human-AI Collaboration in Decision-Making

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Baduna, Maryam; Hylving, Lena

Abstract: This literature review examines trust in AI-augmented decision-making systems i ... Expand

Abstract: This literature review examines trust in AI-augmented decision-making systems in collaborative settings. The analysis suggests that human characteristics, AI characteristics, the decision-making environment, and the collaboration setting are antecedents of human trust in AI. This literature review contributes to the theoretical knowledge of trust with a dynamic learned trust framework, which represents an integrated understanding of trust in AI in human-AI collaborative decision-making. The review also provides practical implications for AI designers and implementors for improving the trustworthiness and adoption of AI for human-AI collaboration. By providing insight for researchers and audiences in information systems, organization and management, this paper can serve as a starting point for investigating the nuances, complexities and dynamics of trust in human-AI relationships. Collapse

Topics: decision making artificial intelligence human AI collaboration human computer interaction expert system

Methods: literature study literature sample computational algorithm synthesis machine learning

What Makes Digital Technology? A Categorization Based on Purpose

2023 | Communications of the Association for Information Systems | Citations: 0

Authors: Baier, Marie-Sophie; Berger, Stephan; Kreuzer, Thomas; Oberländer, Anna; Röglinger, Maximilian

Abstract: Digital technology (DT) is creating and shaping today's world. Building on its ... Expand

Abstract: Digital technology (DT) is creating and shaping today's world. Building on its identity and history of technology research, the Information Systems discipline is at the forefront of understanding the nature of DT and related phenomena. Understanding the nature of DT requires understanding its purposes. Because of the growing number of DTs, these purposes are diversifying, and further examination is needed. To that end, we followed an organizational systematics paradigm and present a taxonomic theory for DT that enables its classification through its diverse purposes. The taxonomic theory comprises a multi-layer taxonomy of DT and purpose-related archetypes, which we inferred from a sample of 92 real-world DTs. In our empirical evaluation, we assessed the reliability, validity, and usefulness of the taxonomy and archetypes. The taxonomic theory exceeds existing technology classifications by being the first that (1) has been rigorously developed, (2) considers the nature of DT, (3) is sufficiently concrete to reflect the diverse purposes of DT, and (4) is sufficiently abstract to be persistent. Our findings add to the descriptive knowledge on DT, advance our understanding of the diverse purposes of DT, and lay the ground for further theorizing. Our work also supports practitioners in managing and designing DTs. Collapse

Topics: knowledge base perceived usefulness competitive advantage decision making technology adoption

Methods: cluster analysis theory development longitudinal data analysis focus group computational algorithm

Theories: information systems theory sociomaterialism theory

Expl(AI)ned: The Impact of Explainable Artificial Intelligence on Users’ Information Processing

2023 | Information Systems Research | Citations: 0

Authors: Bauer, Kevin; von Zahn, Moritz; Hinz, Oliver

Abstract: Because of a growing number of initiatives and regulations, predictions of moder ... Expand

Abstract: Because of a growing number of initiatives and regulations, predictions of modern artificial intelligence (AI) systems increasingly come with explanations about why they behave the way they do. In this paper, we explore the impact of feature-based explanations on users’ information processing. We designed two complementary empirical studies where participants either made incentivized decisions on their own, with the aid of opaque predictions, or with explained predictions. In Study 1, laypeople engaged in the deliberately abstract investment game task. In Study 2, experts from the real estate industry estimated listing prices for real German apartments. Our results indicate that the provision of feature-based explanations paves the way for AI systems to reshape users’ sense making of information and understanding of the world around them. Specifically, explanations change users’ situational weighting of available information and evoke mental model adjustments. Crucially, mental model adjustments are subject to the confirmation bias so that misconceptions can persist and even accumulate, possibly leading to suboptimal or biased decisions. Additionally, mental model adjustments create spillover effects that alter user behavior in related yet disparate domains. Overall, this paper provides important insights into potential downstream consequences of the broad employment of modern explainable AI methods. In particular, side effects of mental model adjustments present a potential risk of manipulating user behavior, promoting discriminatory inclinations, and increasing noise in decision making. Our findings may inform the refinement of current efforts of companies building AI systems and regulators that aim to mitigate problems associated with the black-box nature of many modern AI systems. History: Alessandro Acquisti, senior editor; Jason Chan, associate editor. Funding: This work was supported by the Deutsche Forschungsgemeinschaft (DFG) (Projek 449023539), Volkswagen Foundation (ML2MT), and LeibnizInstitute for Financial Research SAFE. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2023.1199. Collapse

Topics: explainable artificial intelligence artificial intelligence shapley additive explanation local interpretable model agnostic explanation decision making

Methods: F-test regression analysis method experiment field study robustness check

The state of eHealth research across information technologies: A longitudinal analysis using topic modeling to address future scholarship

2023 | HICSS | Citations: 0

Authors: Britt, R.; Chou, S.; Omah, O.; Chakraborty, A.

Abstract: eHealth research has been marked by the last two decades of scholarship spurred ... Expand

Abstract: eHealth research has been marked by the last two decades of scholarship spurred by technological advances and the potential of health promotion and behavior change.The study examined the state of eHealth scholarship across social, behavioral and information technologies through a systematic, machine-based learning approach of the last 19 years across 811 articles. The study analyzes topics that were published using latent Dirichlet allocation of studies from 2002 to 2021; it also raises ethical challenges for researchers related to those in prior health initiatives by the CDC and in current scholarship. Results show the common topics, terms, and linguistic attributes within the state of eHealth scholarship and disparities in other areas based on topics published. Suggestions are offered for interdisciplinary collaboration to facilitate the growth and the optimal, practical use of eHealth and directions for the future. Collapse

Topics: electronic health database system pandemic Python health information system

Methods: topic model literature sample latent dirichlet allocation meta analysis synthesis

Theories: technology acceptance model