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Data sharing concepts : a viable system model diagnosis
Igor Perko, 2023, original scientific article

Abstract: Purpose Artificial intelligence (AI) reasoning is fuelled by high-quality, detailed behavioural data. These can usually be obtained by the biometrical sensors embedded in smart devices. The currently used data collecting approach, where data ownership and property rights are taken by the data scientists, designers of a device or a related application, delivers multiple ethical, sociological and governance concerns. In this paper, the author is opening a systemic examination of a data sharing concept in which data producers execute their data property rights. Design/methodology/approach Since data sharing concept delivers a substantially different alternative, it needs to be thoroughly examined from multiple perspectives, among them: the ethical, social and feasibility. At this stage, theoretical examination modes in the form of literature analysis and mental model development are being performed. Findings Data sharing concepts, framework, mechanisms and swift viability are examined. The author determined that data sharing could lead to virtuous data science by augmenting data producers' capacity to govern their data and regulators' capacity to interact in the process. Truly interdisciplinary research is proposed to follow up on this research. Research limitations/implications Since the research proposal is theoretical, the proposal may not provide direct applicative value but is largely focussed on fuelling the research directions. Practical implications For the researchers, data sharing concepts will provide an alternative approach and help resolve multiple ethical considerations related to the internet of things (IoT) data collecting approach. For the practitioners in data science, it will provide numerous new challenges, such as distributed data storing, distributed data analysis and intelligent data sharing protocols. Social implications Data sharing may post significant implications in research and development. Since ethical, legislative moral and trust-related issues are managed in the negotiation process, data can be shared freely, which in a practical sense expands the data pool for virtuous research in social sciences. Originality/value The paper opens new research directions of data sharing concepts and space for a new field of research.
Keywords: hybrid reality, data sharing, systems thinking, cybernetics, artificial intelligence
Published in DKUM: 14.02.2024; Views: 51; Downloads: 3
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Multi-criteria measurement of ai support to project management
Vesna Čančer, Polona Tominc, Maja Rožman, 2023, original scientific article

Abstract: This paper aims to measure the level of artificial intelligence (AI) support to project management (PM) in selected service sector activities. The exploratory factor analysis was employed based on the extensive survey on AI in Slovenian companies and the multi-criteria measurement with an emphasis on value functions and pairwise comparisons in the analytic hierarchy process. The synthesis and performance sensitivity analysis results show that in the service sector, concerning all criteria, PM is with the level 0.276 best supported with AI in services of professional, scientific, and technical activities, which also stand out concerning the first-level goals in using AI solutions in a project with the value 0.284, and in successful project implementation using AI with the value 0.301. Although the lowest level of AI support to PM, which is 0.220, is in services of wholesale and retail trade and repair of motor vehicles and motorcycles, these services excel in adopting AI technologies in a project with a value of 0.277. Services of financial and insurance activities, with the level 0.257 second-ranked concerning all criteria, have the highest value of 0.269 concerning the first-level goal of improving the work of project leaders using AI. The paper, therefore, contributes to the comparison of AI support to PM in service sector activities. The results can help AI development policymakers determine which activities need to be supported and which should be set as an example. The presented methodological frame can serve to perform measurements and benchmarking in various research fields.
Keywords: artificial intelligence, factor analysis, multiple criteria, performance sensitivity, project management
Published in DKUM: 12.02.2024; Views: 77; Downloads: 8
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Assessing Perceived Trust and Satisfaction with Multiple Explanation Techniques in XAI-Enhanced Learning Analytics
Saša Brdnik, Vili Podgorelec, Boštjan Šumak, 2023, original scientific article

Abstract: This study aimed to observe the impact of eight explainable AI (XAI) explanation techniques on user trust and satisfaction in the context of XAI-enhanced learning analytics while comparing two groups of STEM college students based on their Bologna study level, using various established feature relevance techniques, certainty, and comparison explanations. Overall, the students reported the highest trust in local feature explanation in the form of a bar graph. Additionally, master's students presented with global feature explanations also reported high trust in this form of explanation. The highest measured explanation satisfaction was observed with the local feature explanation technique in the group of bachelor's and master's students, with master's students additionally expressing high satisfaction with the global feature importance explanation. A detailed overview shows that the two observed groups of students displayed consensus in favored explanation techniques when evaluating trust and explanation satisfaction. Certainty explanation techniques were perceived with lower trust and satisfaction than were local feature relevance explanation techniques. The correlation between itemized results was documented and measured with the Trust in Automation questionnaire and Explanation Satisfaction Scale questionnaire. Master's-level students self-reported an overall higher understanding of the explanations and higher overall satisfaction with explanations and perceived the explanations as less harmful.
Keywords: explainable artificial intelligence, learning analytics, XAI techniques, trust, explanation satisfaction
Published in DKUM: 12.02.2024; Views: 80; Downloads: 8
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Artificial-intelligence-supported reduction of employees’ workload to increase the company’s performance in today’s VUCA environment
Maja Rožman, Dijana Oreški, Polona Tominc, 2023, original scientific article

Abstract: This paper aims to develop a multidimensional model of AI-supported employee workload reduction to increase company performance in today's VUCA environment. Multidimensional constructs of the model include several aspects of artificial intelligence related to human resource management: AI-supported organizational culture, AI-supported leadership, AI-supported appropriate training and development of employees, employees' perceived reduction of their workload by AI, employee engagement, and company's performance. The main survey involved 317 medium-sized and large Slovenian companies. Structural equation modeling was used to test the hypotheses. The results show that three multidimensional constructs (AI-supported organizational culture, AI-supported leadership, and AI-supported appropriate training and development of employees) have a statistically significant positive effect on employees' perceived reduction of their workload by AI. In addition, employees' perceived reduced workload by AI has a statistically significant positive effect on employee engagement. The results show that employee engagement has a statistically significant positive effect on company performance. The concept of engagement is based on the fact that the development and growth of the company cannot be achieved by increasing the number of employees or by adding capital; the added value comes primarily from increased productivity, which is a result of the innovative ability of employees and their work engagement, which improve the company's performance. The results will significantly contribute to creating new views in the field of artificial intelligence and adopting important decisions in creating working conditions for employees in today's rapidly changing work environment.
Keywords: artificial intelligence, leadership, employee engagement, company performance
Published in DKUM: 02.02.2024; Views: 88; Downloads: 16
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The potential of ai-driven assistants in scaled agile software development
Vasilka Saklamaeva, Luka Pavlič, 2024, original scientific article

Abstract: Scaled agile development approaches are now used widely in modern software engineering, allowing businesses to improve teamwork, productivity, and product quality. The incorporation of artificial intelligence (AI) into scaled agile development methods (SADMs) has emerged as a potential strategy in response to the ongoing demand for simplified procedures and the increasing complexity of software projects. This paper explores the intersection of AI-driven assistants within the context of the scaled agile framework (SAFe) for large-scale software development, as it stands out as the most widely adopted framework. Our paper pursues three principal objectives: (1) an evaluation of the challenges and impediments encountered by organizations during the implementation of SADMs, (2) an assessment of the potential advantages stemming from the incorporation of AI in large-scale contexts, and (3) the compilation of aspects of SADMs that AI-driven assistants enhance. Through a comprehensive systematic literature review, we identified and described 18 distinct challenges that organizations confront. In the course of our research, we pinpointed seven benefits and five challenges associated with the implementation of AI in SADMs. These findings were systematically categorized based on their occurrence either within the development phase or the phases encompassing planning and control. Furthermore, we compiled a list of 15 different AI-driven assistants and tools, subjecting them to a more detailed examination, and employing them to address the challenges we uncovered during our research. One of the key takeaways from this paper is the exceptional versatility and effectiveness of AI-driven assistants, demonstrating their capability to tackle a broader spectrum of problems. In conclusion, this paper not only sheds light on the transformative potential of AI, but also provides invaluable insights for organizations aiming to enhance their agility and management capabilities.
Keywords: SAFe, scaled agile framework, AI, artificial intelligence, tools, assistants, agile, large-scale
Published in DKUM: 26.01.2024; Views: 72; Downloads: 8
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Artificial intelligence based prediction of diabetic foot risk in patients with diabetes : a literature review
Lucija Gosak, Adrijana Svenšek, Mateja Lorber, Gregor Štiglic, 2023, review article

Abstract: Diabetic foot is a prevalent chronic complication of diabetes and increases the risk of lower limb amputation, leading to both an economic and a major societal problem. By detecting the risk of developing diabetic foot sufficiently early, it can be prevented or at least postponed. Using artificial intelligence, delayed diagnosis can be prevented, leading to more intensive preventive treatment of patients. Based on a systematic literature review, we analyzed 14 articles that included the use of artificial intelligence to predict the risk of developing diabetic foot. The articles were highly heterogeneous in terms of data use and showed varying degrees of sensitivity, specificity, and accuracy. The most used machine learning techniques were support vector machine (SVM) (n = 6) and K-Nearest Neighbor (KNN) (n = 5). Future research is recommended on larger samples of participants using different techniques to determine the most effective one.
Keywords: artificial intelligence, machine learning, thermography, diabetic foot prediction, diabetes, diabetes care, diabetic foot, literature review
Published in DKUM: 27.11.2023; Views: 228; Downloads: 10
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Employing AI Tools in Tourism: A Qualitative Study of Social Media Content Generation in a Work Environment : a qualitative study of social media content generation in a work environment
Tarik Džinić, 2023, undergraduate thesis

Abstract: In the midst of industries being reshaped by the emergence of technologies our attention is drawn to the increasing trend of AI and its potential integration, in the work environment. At present, we recognise the swift evolution of technology and the necessity to align with current trends. In this thesis we delve into the world of content creation in tourism social media marketing. We specifically focus on how AI tools, like ChatGPT transform the way we generate content across social media platforms. Through the thesis we highlight the importance of content marketing in boosting brand visibility and engagement. We further explore how generative AI models can create content, enhance personalization and streamline marketing strategies. Additionally, we investigate the newly emerging field of prompt engineering, which focuses on improved interactions with generative AI models. While AI tools offer efficiency, we emphasize the need to strike a balance between automation and human driven creativity. In the evolving landscape of digital marketing, this study contributes insights into leveraging AI-powered content creation for enriching the tourism industry's social media endeavours.
Keywords: Content creation, artificial intelligence, ChatGPT, prompt engineering, social media marketing
Published in DKUM: 09.10.2023; Views: 402; Downloads: 49
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Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications : a systematic review of the literature
Lucija Gosak, Kristina Martinović, Mateja Lorber, Gregor Štiglic, 2022, review article

Abstract: Aim The aim of this review is to examine the effectiveness of artificial intelligence in predicting multimorbid diabetes-related complications. Background In diabetic patients, several complications are often present, which have a significant impact on the quality of life; therefore, it is crucial to predict the level of risk for diabetes and its complications. Evaluation International databases PubMed, CINAHL, MEDLINE and Scopus were searched using the terms artificial intelligence, diabetes mellitus and prediction of complications to identify studies on the effectiveness of artificial intelligence for predicting multimorbid diabetes-related complications. The results were organized by outcomes to allow more efficient comparison. Key issues Based on the inclusion/exclusion criteria, 11 articles were included in the final analysis. The most frequently predicted complications were diabetic neuropathy (n = 7). Authors included from two to a maximum of 14 complications. The most commonly used prediction models were penalized regression, random forest and Naïve Bayes model neural network. Conclusion The use of artificial intelligence can predict the risks of diabetes complications with greater precision based on available multidimensional datasets and provides an important tool for nurses working in preventive health care. Implications for Nursing Management Using artificial intelligence contributes to a better quality of care, better autonomy of patients in diabetes management and reduction of complications, costs of medical care and mortality.
Keywords: artificial intelligence, prediction models, diabetes, prediction of diabetes complications
Published in DKUM: 03.10.2023; Views: 192; Downloads: 29
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Future tendencies of non-fungible tokens
Nenad Tomić, Violeta Todorović, Milena Jakšić, 2023, original scientific article

Abstract: Blockchain has been one of the key innovations in information technology in the last 15 years. An important aspect of applying blockchain technology is the creation of so-called non-fungible tokens (NFTs). Although the name resembles cryptocurrencies because of the word token, in practice, NFTs do not represent electronic money but a digital certificate of ownership of an asset. They effectively behave like tokens whose total supply is one, and it is immutable. Considering their technical and conceptual basis, NFTs can be defined as digital certificates of ownership based on blockchain tech- nology, the possession of which proves the indisputable ownership of the purchased digital asset. The subject of this paper is the conceptual basis of NFTs and the scope of their application in digital business. It aims to determine the value factors of NFTs and whether an expansion of their use can be expected in the future. The results of our research show that the essential advantage that NFTs bring to digital business is authentication. NFTs also enable the continuous collection of royalties by the author. The last, but potentially most powerful value generator of NFTs, is the creation of an ecosystem, where an online community is formed based on the initial forms of digital assets. Without standardization and regulation by states, NFTs will remain in the market niche of intensive Internet users
Keywords: artificial intelligence, blockchain, cryptocurrencies, non-fungible tokens, exclusivity
Published in DKUM: 05.09.2023; Views: 230; Downloads: 4
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Non-stupidity condition and pragmatics in artificial intelligence
Bojan Borstner, Niko Šetar, 2022, original scientific article

Abstract: Symbol Grounding Problem (SGP) (Harnad 1990) is commonly considered one of the central challenges in the philosophy of artificial intelligence as its resolution is deemed necessary for bridging the gap between simple data processing and understanding of meaning and language. SGP has been addressed on numerous occasions with varying results, all resolution attempts having been severely, but for the most part justifiably, restricted by the Zero Semantic Commitment Condition (Taddeo and Floridi 2005). A further condition that demands explanatory power in terms of machine-to-human communication is the Non-Stupidity Condition (Bringsjord 2013) that demands an SG approach to be able to account for plausibility of higher-level language use and understanding, such as pragmatics. In this article, we undertake the endeavour of attempting to explain how merging certain early requirements for SG, such as embodiment, environmental interaction (Ziemke 1998), and compliance with the Z-Condition with symbol emergence (Sun 2000; Tangiuchi et al. 2016, etc.) rather than direct attempts at symbol grounding can help emulate human language acquisition (Vogt 2004; Cowley 2007). Along with the presumption that mind and language are both symbolic (Fodor 1980) and computational (Chomsky 2017), we argue that some rather abstract aspects of language can be logically formalised and finally, that this melange of approaches can yield the explanatory power necessary to satisfy the Non-Stupidity Condition without breaking any previous conditions.
Keywords: artificial intelligence, symbol arguing, pragmatics, language, computationalism
Published in DKUM: 18.08.2023; Views: 202; Downloads: 25
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