Situational Factor Determinants of the Allocation of Decision Rights to Edge Computers

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Chua, Cecil Eng Huang; Niederman, Fred

Abstract: Internet of Things (IoT) designers frequently must determine whether action-orie ... Expand

Abstract: Internet of Things (IoT) designers frequently must determine whether action-oriented decisions should be made by edge computers or whether they should be made only by central servers combining input from all edge computers. An important example of this design problem occurs in fire protection IoT, where individual edge computers attached to sensors might be empowered to make decisions (have decision rights) about how to manage the fire. Alternatively, decision rights could be held exclusively by a central server isolated from the fire, because the designer is concerned damage to edge computers could cause them to act unreliably. This research models this allocation of decision rights to identify the relative influence of various decision factors. We first model the allocation of decision rights under the following assumptions: (1) The central server cannot make an error the edge computer cannot make; (2) the central server cannot update the edge computer with its information in a timely manner; and (3) the central server cannot reverse an action initiated by the edge computer to explore the factors impacting decision rights conferral. We then relax each of these three assumptions. We show how relaxing each assumption radically changes the factors impacting decision rights conferral. We also show that allowing the central server to update information on the edge computer or reverse the edge computer's decision making can result in overall lower system performance. We then perform a series of numerical experiments to understand how changing various parameters affect the problem. We show for the general real-world scenario, the key factor influencing the decision is the ability of the edge computer to detect false alarms. We also show magnitude of loss and ratio of real to false incidents have a linear and logarithmic relationship to the reliability of the edge computer. Collapse

Topics: decision making internet of things human in the loop human computer interaction edge computing

Methods: experiment computational algorithm theoretical contribution simulation

Automated Deductive Content Analysis of Text: A Deep Contrastive and Active Learning Based Approach

2023 | International Conference on Information Systems | Citations: 0

Authors: De Oliveira Silveira, Alysson; Ray, Arindam; Ebrahimi, Mohammadreza; Bhattacherjee, Anol

Abstract: Content analysis traditionally involves human coders manually combing through t ... Expand

Abstract: Content analysis traditionally involves human coders manually combing through text documents to search for relevant concepts and categories. However, this approach is time-intensive and not scalable, particularly for secondary data like social media content, news articles, or corporate reports. To address this problem, the paper presents an automated framework called Automated Deductive Content Analysis of Text (ADCAT) that uses deep learning-based semantic techniques, an ontology of validated construct measures, a large language model, human-in-the-loop disambiguation, and a novel augmentation-based weighted contrastive learning approach for improved language representations, to build a scalable approach for deductive content analysis. We demonstrate the effectiveness of the proposed approach to identify firm innovation strategies from their 10-K reports to obtain inferences reasonably close to human coding. Collapse

Topics: knowledge representation human in the loop human computer interaction innovation management organizational innovation

Methods: ontological modelling machine learning language model large language model qualitative content analysis

Artificial Intelligence (AI) and Information Systems: Perspectives to Responsible AI

2023 | Information Systems Frontiers | Citations: 2

Authors: Dennehy, Denis; Griva, Anastasia; Pouloudi, Nancy; Dwivedi, Yogesh K.; Mäntymäki, Matti; Pappas, Ilias O.

Abstract: example the responsible design and governance (Mäntymäki et al., 2022b) of AI ... Expand

Abstract: example the responsible design and governance (Mäntymäki et al., 2022b) of AI systems. While organisations are increasingly investing in ethical AI and Responsible AI (RAI) (Zimmer et al., 2022), recent reports suggest that this comes at a cost and may lead to burnout in responsible-AI teams (Heikkilä, 2022). Thus, it is critical to consider how we educate about RAI (Grøder et al., 2022) and rethink our traditional learning designs (Pappas & Giannakos, 2021), as this can influence end-users' perceptions towards AI applications (Schmager et al., 2023) as well as how future employees approach the design and implementation of AI applications (Rakova et al., 2021;Vassilakopoulou et al., 2022).The use of algorithmic decision-making and decisionsupport processes, particularly AI is becoming increasingly pervasive in the public sector, also in high-risk application areas such as healthcare, traffic, and finance (European Commission, 2020). Against this backdrop, there is growing concern over the ethical use and safety of AI, fuelled by reports of ungoverned military applications (Butcher and Beridze, 2019; Dignum, 2020), privacy violations attributed to facial recognition technologies used by the police (Rezende, 2022), unwanted biases exhibited by AI applications used by courts (Imai et al., 2020), and racial biases in clinical algorithms (Vyas et al. 2020). The opacity and lack of explainability frequently attributed to AI systems makes evaluating the trustworthiness of algorithmic decisions challenging even for technical experts, let alone the public. Together with the algorithm-propelled proliferation of misinformation, hate speech and polarising content on social media platforms, there is a high risk for erosion of trust in algorithmic systems used by the public sector (Janssen et al., 2020). Ensuring that people can trust in the algorithmic processes is essential not only for reaping the potential benefits from AI (Dignum, 2020) but also for fostering trust and resilience at a societal level.AI researchers and practitioners have expressed their fears about AI systems being developed that are Collapse

Topics: artificial intelligence IT governance decision making digital economy online learning system

Methods: chatbot theory development case study qualitative interview literature sample

Theories: technological frames of reference

Artificial intelligence for decision-making and the future of work

2023 | International Journal of Information Management | Citations: 0

Authors: Dennehy, Denis; Griva, Anastasia; Pouloudi, Nancy; Mäntymäki, Matti; Pappas, Ilias

Abstract: ... Expand

Abstract: Collapse

Topics: artificial intelligence human computer interaction human in the loop data governance data quality

Methods: chatbot case study theory development analytic hierarchy process trade-off analysis method

Theories: institutional theory

Promoting Learning Through Explainable Artificial Intelligence: An Experimental Study in Radiology

2023 | International Conference on Information Systems | Citations: 0

Authors: Ellenrieder, Sara; Kallina, Emma; Pumplun, Luisa; Gawlitza, Joshua Felix; Ziegelmayer, Sebastian; Buxmann, Peter

Abstract: The deployment of machine learning (ML)-based decision support systems (DSSs) i ... Expand

Abstract: The deployment of machine learning (ML)-based decision support systems (DSSs) in high-risk environments such as radiology is increasing. Despite having achieved high decision accuracy, they are prone to errors. Thus, they are primarily used to assist radiologists in their decision making. However, collaborative decision making poses risks to the decision maker, e.g. automation bias and long-term performance degradation. To address these issues, we propose combining findings of the research streams of explainable artificial intelligence and education to promote human learning through interaction with ML-based DSSs. We provided radiologists with explainable vs nonexplainable decision support that was high-vs low-performing in a between-subject experimental study to support manual segmentation of 690 brain tumor scans. Our results show that explainable ML-based DSSs improved human learning outcomes and prevented false learning triggered by incorrect decision support. In fact, radiologists were able to learn from errors made by the low-performing explainable ML-based DSS. Collapse

Topics: decision support system decision support explainable artificial intelligence decision making artificial intelligence

Methods: experiment qualitative interview magnetic resonance imaging parametric test ANOVA

Responsible cognitive digital clones as decision-makers: a design science research study

2023 | European Journal of Information Systems | Citations: 0

Authors: Golovianko, Mariia; Gryshko, Svitlana; Terziyan, Vagan; Tuunanen, Tuure

Abstract: This study uses a design science research methodology to develop and evaluate th ... Expand

Abstract: This study uses a design science research methodology to develop and evaluate the Pi-Mind agent, an information technology artefact that acts as a responsible, resilient, ubiquitous cognitive clone – or a digital copy – and an autonomous representative of a human decision-maker. Pi-Mind agents can learn the decision-making capabilities of their “donors” in a specific training environment based on generative adversarial networks. A trained clone can be used by a decision-maker as an additional resource for one’s own cognitive enhancement, as an autonomous representative, or even as a replacement when appropriate. The assumption regarding this approach is as follows: when someone was forced to leave a critical process because of, for example, sickness, or wanted to take care of several simultaneously running processes, then they would be more confident knowing that their autonomous digital representatives were as capable and predictable as their exact personal “copy”. The Pi-Mind agent was evaluated in a Ukrainian higher education environment and a military logistics laboratory. In this paper, in addition to describing the artefact, its expected utility, and its design process within different contexts, we include the corresponding proof of concept, proof of value, and proof of use. Collapse

Topics: artificial intelligence decision making digital transformation legal context information system use

Methods: design artifact machine learning binary classification design science design principle

Task delegation from AI to humans: A principal-agent perspective

2023 | International Conference on Information Systems | Citations: 0

Authors: Guggenberger, Tobias; Lämmermann, Luis; Urbach, Nils; Walter, Anna; Hofmann, Peter

Abstract: Increasingly intelligent AI artifacts in human-AI systems perform tasks more au ... Expand

Abstract: Increasingly intelligent AI artifacts in human-AI systems perform tasks more autonomously as entities that guide human actions, even changing the direction of task delegation between humans and AI. It has been shown that human-AI systems achieve better results when the AI artifact takes the leading role and delegates tasks to a human rather than the other way around. This study presents phenomena, conflicts, and challenges that arise in this process, explored through the theoretical lens of principalagent theory (PAT). The findings are derived from a systematic literature review and an exploratory interview study and are placed in the context of existing constructs of PAT. Furthermore, this article paper identifies new causes of tensions that arise specifically in AI-to-human delegation and calls for special mechanisms beyond the classical solutions of PAT. The paper thus contributes to the understanding of autonomous AI and its implications for human-AI delegation. Collapse

Topics: artificial intelligence human computer interaction human AI collaboration explainable artificial intelligence software developer

Methods: qualitative interview personal interview structured literature research qualitative coding computational algorithm

Theories: lemon market theory agency theory

The Recursive Theory of Knowledge Augmentation: Integrating human intuition and knowledge in Artificial Intelligence to augment organizational knowledge

2023 | Information Systems Frontiers | Citations: 0

Authors: Harfouche, Antoine; Quinio, Bernard; Saba, Mario; Saba, Peter Bou

Abstract: Artificial intelligence (AI) has increased the ability of organizations to accum ... Expand

Abstract: Artificial intelligence (AI) has increased the ability of organizations to accumulate tacit and explicit knowledge to inform management decision-making. Despite the hype and popularity of AI, there is a noticeable scarcity of research focusing on AI's potential role in enriching and augmenting organizational knowledge. This paper develops a recursive theory of knowledge augmentation in organizations (the KAM model) based on a synthesis of extant literature and a four-year revised canonical action research project. The project aimed to design and implement a human-centric AI (called Project) to solve the lack of integration of tacit and explicit knowledge in a scientific research center (SRC). To explore the patterns of knowledge augmentation in organizations, this study extends Nonaka's SECI (socialization, externalization, combination, and internalization) model by incorporating the human-in-the-loop Informed Artificial Intelligence (IAI) approach. The proposed design offers the possibility to integrate experts' intuition and domain knowledge in AI in an explainable way. The findings show that organizational knowledge can be augmented through a recursive process enabled by the design and implementation of human-in-the-loop IAI. The study has important implications for research and practice. Collapse

Topics: artificial intelligence organizational memory explicit knowledge human in the loop human computer interaction

Methods: computational algorithm action research qualitative interview participant observation statistical hypothesis test

Theories: SECI model of knowledge dimensions organizational knowledge creation

How Today’s AI Content Generators Outperform Average Novice Students in Information Systems Exams

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Hobert, Sebastian; Groth, Michael; Nießner, Tobias; Wilhelmi, Lars

Abstract: The public av ailability of sophisticated AI content generators based on large ... Expand

Abstract: The public av ailability of sophisticated AI content generators based on large -scale language models achiev ed enormous media attention in recent months. AI content generators are expected to have a major impact on future information sy stems developments as well as priv ate and business life. Particularly in the field of education, the question arises as to how these innovations in AI technology will impact teaching, learning, and ex amination. With a specific focus on examinations in the information systems di scipline, we show in this research-in-progress paper, based on an ex perimental field study, how a today's AI content generator performs in solv ing a fundamental information systems exam. Based on our preliminary results, we found that the quality v aries ac ross different exam question ty pes. With this research project, we offer start points and initial guidance on how educators can deal with the av ailability of AI content generators. Collapse

Topics: artificial intelligence human in the loop human computer interaction

Methods: chatGPT case study large language model experiment field study

EXPLORING THE INTERPLAY BETWEEN AI ALGORITHMS, DOCTOR, AND PATIENT IN THE HEALTHCARE CONTEXT

2023 | European Conference On Information Systems | Citations: 0

Authors: Ichkiti, Veriko; Zhang, Zhewei; Nandhakumar, Joe

Abstract: Since the dawn of evolutionary technological civilisation, analytics has promis ... Expand

Abstract: Since the dawn of evolutionary technological civilisation, analytics has promised to revolutionise healthcare and enable more efficient patient care delivery. However, after the applications of AI tools in healthcare, doctors and patients are no longer the sole players in the doctor-patient relationship. AI emerges as the third player, which creates unique opportunities in this relationship. By examining the impact of non-human actor-AI on the doctor-patient relationship and exploring the related concepts, such as human-AI augmentation and human-in-the-loop, valuable insights can be gained regarding the use of AI in healthcare. Following this background, based on a systematic literature review and the methodological approach of Lebovitz et al. (2022), this paper seeks to enhance the knowledge and expertise in healthcare by examining how AI tools shape and are shaped by the doctor-patient relationship when three players (AI, doctors, and patients) are present. Collapse

Topics: artificial intelligence human computer interaction human AI collaboration pseudonymity privacy

Methods: qualitative interview field study literature study personal interview qualitativ observational study