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

“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

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

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

Explainable artificial intelligence in information systems: A review of the status quo and future research directions

2023 | Electronic Markets | Citations: 4

Authors: Brasse, Julia; Broder, Hanna Rebecca; Förster, Maximilian; Klier, Mathias; Sigler, Irina

Abstract: The quest to open black box artificial intelligence (AI) systems evolved into an ... Expand

Abstract: The quest to open black box artificial intelligence (AI) systems evolved into an emerging phenomenon of global interest for academia, business, and society and brought about the rise of the research field of explainable artificial intelligence (XAI). With its pluralistic view, information systems (IS) research is predestined to contribute to this emerging field; thus, it is not surprising that the number of publications on XAI has been rising significantly in IS research. This paper aims to provide a comprehensive overview of XAI research in IS in general and electronic markets in particular using a structured literature review. Based on a literature search resulting in 180 research papers, this work provides an overview of the most receptive outlets, the development of the academic discussion, and the most relevant underlying concepts and methodologies. Furthermore, eight research areas with varying maturity in electronic markets are carved out. Finally, directions for a research agenda of XAI in IS are presented. Collapse

Topics: explainable artificial intelligence artificial intelligence user requirement electronic market user experience

Methods: literature study literature sample cluster analysis synthesis machine learning

Theories: information systems theory activity theory agency theory attribution theory cognitive fit theory

Artificial Intelligence as a Service: Trade-Offs Impacting Service Design and Selection

2023 | International Conference on Information Systems | Citations: 0

Authors: Brecker, Kathrin; Lins, Sebastian; Trenz, Manuel; Sunyaev, Ali

Abstract: AI as a Service (AIaaS) is a promising path to leverage AI capabilities from th ... Expand

Abstract: AI as a Service (AIaaS) is a promising path to leverage AI capabilities from the cloud. However, there is no one-size-fits-all service, but heterogenous service options since interdependent AIaaS characteristics require trade-offs. We lack knowledge on these tradeoffs and how they result from conflicting characteristics. Therefore, we interviewed 39 AIaaS providers, customers, and consultants to provide rich descriptions of interdependent characteristics and uncover resulting trade-offs and their consequences. This study contributes to a better understanding of the inner functioning and interplay of AIaaS characteristics and discusses how this complex nature of service offerings impacts providers' design and customers' selection decisions. Collapse

Topics: artificial intelligence cloud computing ease of use data protection economies of scale

Methods: qualitative interview design artifact constant comparative analysis qualitative coding triangulation

Theories: SERVQUAL core competency theory

Algorithm as Boss or Coworker? Randomized Field Experiment on Algorithmic Control and Collaboration in Gig Platform

2023 | International Conference on Information Systems | Citations: 0

Authors: Chan, Jason; Kyung, Nakyung; Yoon, Sojung; Kim, Yeonseo

Abstract: ... Expand

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Topics: artificial intelligence platform worker decision making mobile application human computer interaction

Methods: computational algorithm survey experiment field experiment robustness check

Introduction to the Minitrack on The Diffusion, Impacts, Adoption and Usage of ICTs upon Society

2023 | HICSS | Citations: 0

Authors: Choudrie, Jyoti; Kurnia, Sherah; Sundaram, David

Abstract: ... Expand

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Topics: artificial intelligence innovation management affordance user experience human AI collaboration

Methods: case study latent dirichlet allocation

Theories: theory of affordance

Augmented Facilitation: Designing a multi-modal Conversational Agent for Group Ideation

2023 | International Conference on Information Systems | Citations: 0

Authors: Cvetkovic, Izabel; Gierlich-Joas, Maren; Tavanapour, Navid; Debowski-Weimann, Nicole; Bittner, Eva

Abstract: Human facilitators face the challenge to structure and collect relevant insight ... Expand

Abstract: Human facilitators face the challenge to structure and collect relevant insights from collaborative creative work sessions, which can suffer if they face a high workload. Hence, for effective value co-creation in organizational ideation we suggest an facilitation augmentation with a conversational agent (CA). CAs have the ability to support respective collaborative work by documenting and analyzing unstructured data. Following the design science research paradigm, and based on the literature about facilitation and human-AI collaboration, we derive design principles to develop a CA prototype that collects ideas from a group ideation session and displays them back in a structured (multi-modal) manner. We evaluate the CA by conducting four focus groups. Key findings show that the CA successfully distills and enriches information. Our study contributes to understanding the role of CA in augmenting facilitation and it provides guidance for practice on how to integrate these technologies in group meetings. Collapse

Topics: SWOT analysis artificial intelligence human AI collaboration human computer interaction participatory design

Methods: design artifact qualitative interview design principle group interview qualitative content analysis

Let Artificial Intelligence Be Your Shelf Watchdog: The Impact of Intelligent Image Processing-Powered Shelf Monitoring on Product Sales

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Deng, Yipu; Zheng, Jinyang; Huang, Liqiang; Kannan, Karthik

Abstract: We collaborated with a leading fast-moving consumer goods (FMCG) manufacturer t ... Expand

Abstract: We collaborated with a leading fast-moving consumer goods (FMCG) manufacturer to investigate how intelligent image processing (IIP)-based shelf monitoring aids manufacturers' shelf management by using data from a quasi-experiment and a field experiment. We discovered that such artificial intelligence (AI) assistance significantly and consistently improves product sales. Several underlying mechanisms were revealed by our quantitative and qualitative analysis. First, retailers are more likely to comply due to the greater monitoring effectiveness enabled by AI assistance. Second, the positive effect of IIP-based shelf monitoring partially persists after it is terminated, implying that human learning takes place. Third, the value of IIP-based shelf monitoring can be attributed to independent retailers rather than chain retailers. Since the degree of contract heterogeneity is the major difference between these retailers in terms of monitoring, this finding further suggests that AI is relatively more scalable when coping with more heterogeneous instances. Apart from these great benefits, we demonstrate the low marginal costs of implementing IIP-powered shelf monitoring, which indicates its long-term applicability and potential to generate incremental value. Our research contributes to several literature streams and provides managerial insights for practitioners who consider AI-assisted operational models. Collapse

Topics: artificial intelligence organizational value cost benefit analysis mobile application human AI collaboration

Methods: experimental group field experiment qualitative interview experiment quasi experiment