Philosophy of artificial intelligence and its place in society:
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| Sprache: | English |
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Hershey, PA
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[2023]
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| Schriftenreihe: | Advances in human and social aspects of technology (AHSAT) book series
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| Beschreibung: | xxii, 439 Seiten Illustrationen 279 mm. |
| ISBN: | 9781668495926 9781668495919 |
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Datensatz im Suchindex
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Table of Contents Preface. xv Chapter 1 Causality: The Next Step in Artificial Intelligence.1 Luis Cavique, Universidade Aberta, Portugal Chapter 2 Impact of Artificial Intelligence on Marketing Research: Challenges and Ethical Considerations. 18 Laura Sdez-Ortuno, University of Barcelona, Spain Javier Sanchez-Garcia, Jaume I University, Spain Santiago Forgas-Coll, University of Barcelona, Spain Rubén Huertas-Garcia, University of Barcelona, Spain Eloi Puertas-Prat, University of Barcelona, Spain Chapter 3 Artificial Intelligence in Healthcare. 43 Kun-Huang Huarng, National Taipei University of Business, Taiwan Tiffany Hui-Kuang Yu, Feng Chia University, Taiwan Duen-Huang Huang, Chaoyang University of Technology, Taiwan Chapter 4 Causal Machine Learning in Social Impact Assessment. 56 Nuno Castro Lopes, Universidade Aberta, Portugal Luis Cavique, Universidade Aberta, Portugal Chapter 5 The Incorporation of Large Language Models (LLMs) in the Field of Education: Ethical Possibilities, Threats, and Opportunities. Paul Aldrin Pineda Dungca, Salesians of Don Bosco, Philippines 78 Chapter 6 Artificial Intelligence in
Tourism. 98 Enrique Bigné, Universität de Valencia, Spain
Chapter 7 The Influence of Culture on Sentiments Expressed in Online Reviews of Eco-Friendly Hotels: The Case Study of Amsterdam.115 Estefania Ballester Chirica, University of Valencia, Spain Carla Ruiz-Mafé, University of Valencia, Spain Natalia Rubio, Autonomous University of Madrid, Spain Chapter 8 Artificial Intelligence Method for the Analysis of Marketing Scientific Literature. 142 Antonio Hyder, Miguel Hernandez University, Spain Hackers and Founders Research, USA Carlos Perez-Vidal, Universidad Miguel Hernandez, Spain Ronjon Nag, Stanford University, USA Chapter 9 Artificial Intelligence for Renewable Energy Systems and Applications: A Comprehensive Review 160 Manikandakumar Muthusamy, New Horizon College of Engineering, India Karthikeyan Periyasamy, Thiagarajar College of Engineering, India Chapter 10 Exploratory Cluster Analysis Using Self-Organizing Maps: Algorithms, Methodologies, and Framework. 187 Nuno C. Marques, NOVA-LINCS, SST, Universidade NOVA de Lisboa, Portugal Bruno Silva, EST, Polytechnic Institute ofSetubal, Portugal Chapter 11 Persons and Personalization on Digital Platforms: A Philosophical Perspective. 214 Travis Greene, Copenhagen Business School, Denmark Galit Shmueli, National Tsing Hua University, Taiwan Chapter 12 Mind
Uploading in Artificial Intelligence.271 Jason Wissinger, Waynesburg University, USA Elizabeth Baoying Wang, Waynesburg University, USA Chapter 13 Ethical Issues of Artificial Intelligence (AI): Strategic Solutions. 283 Sara Shawky, Griffith University, Australia Park Thaichon, University of Southern Queensland, Australia Sara Quach, Griffith University, Australia Lars-Erik Casper Ferm, The University of Queensland, Australia Chapter 14 Intelligence Augmentation via Human-AI Symbiosis: Formulating Wise Systems for a Metasociety. 301 Nikolaos Stylos, University of Bristol, UK
Chapter 15 Artificial Intelligence in Sports: Monitoring Marathons in Social Media - The Role of Sports Events in Territory Branding. 315 Natalia Vila-Lopez, University of Valencia, Spain Ines Kuster-Boluda, University of Valencia, Spain Francisco J. Sarabia-Sanchez, University Miguel Hernandez, Spain Chapter 16 AI-Driven Customer Experience: Factors to Consider. 341 Svetlana Bialkova, Liverpool Business School, UK Chapter 17 Impact of Artificial Intelligence in Industry 4.0 and 5.0. 358 LuizMotinho, University of Suffolk, UK Luis Cavique, Universidade Aberta, Portugal Compilation of References. 377 About the Contributors. 430 Index. 436
Detailed Table of Contents Preface. xv Chapter 1 Causality: The Next Step in Artificial Intelligence. 1 Luis Cavique, Universidade Aberta, Portugal Judea Pearl’s ladder of causation framework has dramatically influenced the understanding of causality in computer science. Despite artificial intelligence (AI) advancements, grasping causal relationships remains challenging, emphasizing the causal revolution’s significance in improving Al’s understanding of cause and effect. The work presents a novel taxonomy of causal inference methods, clarifying diverse approaches for inferring causality from data. It highlights the implications of causality in responsible AI and explainable AI (xAI), addressing bias in AI systems. The chapter points out causality as the next step in AI for creating new questions, developing causal tools, and clarifying opaque models with xAI approaches. The work clarifies causal models’ significance and implications in various AI subareas. Chapter 2 Impact of Artificial Intelligence on Marketing Research: Challenges and Ethical Considerations. 18 Laura Sdez-Ortufio, University of Barcelona, Spain Javier Sanchez-Garcia, Jaume 1 University, Spain Santiago Forgas-Coll, University of Barcelona, Spain Rubén Huertas-Garcia, University of Barcelona, Spain Eloi Puertas-Prat, University of Barcelona, Spain This chapter
explores the use of artificial intelligence (AI) in market research and its potential impact on the field. Discuss how AI can be used for data collection, filtering, analysis, and prediction, and how it can help companies develop more accurate predictive models and personalized marketing strategies. Highlight the drawbacks of AI, such as the need to ensure diverse and unbiased data and the importance of monitoring and interpreting results and covers various AI techniques used in market research, including machine learning, natural language processing, computer vision, deep learning, and rule-based systems. The applications of AI in marketing research are also discussed, including sentiment analysis, market segmentation, predictive analytics, and adaptive recommendation engines and personalization systems. The chapter concludes that while AI presents many benefits, it also presents several challenges related to data quality and accuracy, algorithmic biases and fairness issues, as well as ethical considerations that need to be carefully considered.
Chapter 3 Artificial Intelligence in Healthcare. 43 Kun-Huang Huarng, National Taipei University of Business, Taiwan Tiffany Hui-Kuang Yu, Feng Chia University, Taiwan Duen-Huang Huang, Chaoyang University of Technology, Taiwan Artificial intelligence (AI) has been applied to various domains to improve the quality of human life. This chapter outlines the recent application of AI in healthcare. A brief history of AI development is first introduced. Machine learning, one of the current AI advancements, is explained. Successful AI application in different areas of healthcare is then showcased, including different medical diagnosis and long-term care. The popular ChatGPT series of systems and their extraordinary performance are described. This chapter ends with debates and future expectations of AI. Chapter 4 Causal Machine Learning in Social Impact Assessment. 56 Nuno Castro Lopes, Universidade Aberta, Portugal Luis Cavique, Universidade Aberta, Portugal Social impact assessment is a fundamental process to verify the achievement of the objectives of interventions and, consequently, to validate investments in the social area. Generally, this process is based on the analysis of the average effects of the intervention, which does not allow a detailed understanding of the individualization of these effects. Causal machine learning methods mark an evolution in causal inference, as they allow for a
more heterogeneous assessment of the effects of interventions. Applying these methods to evaluate the impact of social projects and programs offers the advantage of improving the selection of target audiences and optimizing and personalizing future interventions. In this chapter, in a non-technical way, the authors explore classical causal inference methods to estimate average effects and new causal machine learning methods to evaluate heterogeneous effects. They address adapting the Uplift Modeling method to assess social interventions. They also address the advantages, limitations, and research needs for using these new techniques in social intervention. Chapter 5 The Incorporation of Large Language Models (LLMs) in the Field of Education: Ethical Possibilities, Threats, and Opportunities.78 Paul Aldrin Pineda Dungca, Salesians of Don Bosco, Philippines This chapter delves into the ethical implications that arise from integrating LLMs within the realm of education. LLMs, exemplified by the GPT-3.5, have emerged as formidable instruments for natural language processing, offering diverse applications in educational domains. Nevertheless, their adoption necessitates careful consideration of ethical matters. This chapter comprehensively overviews the ethical potentials, threats, and opportunities in incorporating LLMs into education. It scrutinizes the potential advantages, including enriched personalized learning experiences and enhanced accessibility, while addressing
concerns regarding data privacy, bias, and the ramifications of supplanting human instructors. By critically examining the ethical dimensions, this chapter endeavors to foster a varied comprehension of the implications of utilizing LLMs in educational settings.
Chapter 6 Artificial Intelligence in Tourism. 98 Enrique Bigné, Universität de Valencia, Spain Artificial intelligence in tourism activities opens a bundle of emerging applications for tourists and companies. This chapter aims to delineate the stages of the tourist journey and the usage of four types of intelligence suggested in the literature: mechanical, analytical, intuitive, and empathetic. Based on these two ideas, the authors propose a useful framework for disentangling the different types of current and future applications of AI in tourism. Each stage involves multiple suppliers with different types of AI applications, and its adoption will ultimately rely on tourist trust and, therefore, willingness to share data and the use of robotics and other AI forms. The chapter ends with some trends and reflections on the expansion of AI in tourism that pivot around these ideas: job replacement and flexible operations; mobile-centric approach; data integration and analytics; revenue management and customer interactions tension; (v) neuroscientific tools for AI in tourism. Chapter 7 The Influence of Culture on Sentiments Expressed in Online Reviews of Eco-Friendly Hotels: The Case Study of Amsterdam. 115 Estefania Ballester Chirica, University of Valencia, Spain Carla Ruiz-Mafé, University of Valencia, Spain Natalia Rubio, Autonomous University of
Madrid, Spain The proliferation of content generated by tourists, in parallel with the exponential growth of social media is causing a paradigm shift in research. Traditional surveys cannot be necessary to obtain users’ opinions when scholars can access this valuable information freely through social media. In the domain of tourism, online tourists’ reviews (OTRs) shared on online travel communities stand out. The aim of this study is to demonstrate the usefulness of OTRs in analysing the image of a green hotel. The authors also examine the possible differences in the content of green hotel online reviews across Anglos and European tourists. The data source are 28,189 reviews by tourists shared on TripAdvisor regarding the 82 green hotels of the city of Amsterdam. The findings showed that tourist’s culture significantly determine the content of the OTRs. The results show preferences and opinions from the tourist’s perspective, which can be useful for hotel managers to promoting sustainability practices. Chapter 8 Artificial Intelligence Method for the Analysis of Marketing Scientific Literature. 142 Antonio Hyder, Miguel Hernandez University, Spain Hackers and Founders Research, USA Carlos Perez-Vidal, Universidad Miguel Hernandez, Spain Ronjon Nag, Stanford University, USA A machine-based research reading methodology specific to the academic discipline of marketing science is introduced, focused on the text mining of scientific texts, analysis and predictive writing, by adopting artificial intelligence developments from other research fields
in particular materials and chemical science. It is described how marketing research can be extracted from documents, classified and tokenised in individual words. This is conducted by applying text-mining with named entity recognition together with entity normalisation for large-scale information extraction of published scientific literature. Both a generic methodology for overall marketing science analysis as well as a narrowed-down contextualised method for delimited marketing topics are detailed. Automated literature review is discussed as well
as potential automated formulation of hypotheses and how AI can assist in the transfer of marketing research knowledge to practice, in particular to startups, as they can benefit from AI powered science based decision making. Recommendations for next steps are made. Chapter 9 Artificial Intelligence for Renewable Energy Systems and Applications: A Comprehensive Review 160 Manikandakumar Muthusamy, New Horizon College of Engineering, India Karthikeyan Periyasamy, Thiagarajar College of Engineering, India Artificial Intelligence technology has advanced tremendously in recent years, and it is now widely used in a variety of fields, including energy, agriculture, geology, information processing, medicine, defence systems, space research and exploration, marketing, and many more. The introduction of artificial intelligence technology has ushered in a new era of renewable energy systems and smart power grid modernization. It assists in attaining the intended system availability, reliability, power quality, efficiency, and security goals through optimal resource utilization and cost-effective electricity. Automated power generation systems, energy storage control, wind turbine aerodynamic performance optimization, power generator efficiency enhancement, health monitoring of renewable energy generation systems, and fault detection and diagnose in a smart grid subsystem are just a few of the applications. The main aim of this proposed chapter is to demonstrate how artificial intelligence techniques play a significant role in renewable energy systems with their diverse applications.
Chapter 10 Exploratory Cluster Analysis Using Self-Organizing Maps: Algorithms, Methodologies, and Framework. 187 Nuno C. Marques, NOVA-LINCS, SST, Universidade NOVA de Lisboa, Portugal Bruno Silva, EST, Polytechnic Institute ofSetubal, Portugal As the volume and complexity of data streams continue to increase, exploratory cluster analysis is becoming increasingly important. In this chapter, the authors explore the use of artificial neural networks (ANNs), particularly self-organizing maps (SOMs), for this purpose. They propose additional methodologies, including concept drift detection, as well as distributed and collaborative learning strategies and introduce a new open-source Java ANN library, designed to support practical applications of SOMs across various domains. By following our tutorial, users will gain practical insights into visualizing and analyzing these challenging datasets, enabling them to harness the full potential of our approach in their own projects. Overall, this chapter aims to provide readers with a comprehensive understanding of SOMs and their place within the broader context of artificial neural networks. Furthermore, we offer practical guidance on the effective development and utilization of these models in real-world applications. Chapter 11 Persons and Personalization on Digital Platforms: A Philosophical Perspective. 214 Travis Greene, Copenhagen Business School,
Denmark Galit Shmueli, National Tsing Hua University, Taiwan This chapter explores personalization and its connection to the philosophical concept of the person, arguing that a deeper understanding of the human person and a good society is essential for ethical personalization. Insights from artificial intelligence (AI), philosophy, law, and more are employed to examine personalization technology. The authors present a unified view of personalization as automated
control of human environments through digital platforms and new forms of AI, while also illustrating how platforms can use personalization to control and modify persons’ behavior. The ethical implications of these capabilities are discussed in relation to concepts of personhood to autonomy, privacy, and self-determination within European AI and data protection law. Tentative principles are proposed to better align personalization technology with democratic values, and future trends in personalization for business and public policy are considered. Overall, the chapter seeks to uncover unresolved tensions among philosophical, technological, and economic viewpoints of personalization. Chapter 12 Mind Uploading in Artificial Intelligence.271 Jason Wissinger, Waynesburg University, USA Elizabeth Baoying Wang, Waynesburg University, USA Mind uploading is the futurist idea of emulating all brain processes of an individual on a computer. Progress towards achieving this technology is currently limited by society’s capability to study the human brain and the development of complex artificial neural networks capable of emulating the brain’s architecture. The goal of this chapter is to provide a brief history of both categories, discuss the progress made, and note the roadblocks hindering future research. Then, by examining the roadblocks of neuroscience and artificial intelligence together, this chapter will outline a way to overcome their respective limitations by using the other
field’s strengths. Chapter 13 Ethical Issues of Artificial Intelligence (AI): Strategic Solutions. 283 Sara Shawky, Griffith University, Australia Park Thaichon, University of Southern Queensland, Australia Sara Quach, Griffith University, Australia Lars-Erik Casper Perm, The University of Queensland, Australia Ethical issues of AI have become a huge concern dominating government, media, and academic discourse. This chapter sheds light on some of the most pressing ethical issues that result from the adoption of AIpowered tools. Increasing inequality, widening social and economic gaps, compromising privacy and data protection, outsmarting humans and impacting human rights, lack of accountability, liability and reliability, and lack of empathy and sympathy are considered the most pressing challenges that need to be addressed concerning AI and big data. This chapter also provides insight into strategies that are currently in place to overcome adverse implications of AI in the public and private sectors. Providing insight into these ethical challenges along with the governing solutions makes a significant contribution to the ongoing discourse and urges for bringing forth sustainable solutions that are necessary for the ethical application of these technologies in different fields. Chapter 14 Intelligence Augmentation via Human-AI Symbiosis: Formulating Wise Systems for a
Metasociety. 301 Nikolaos Stylos, University of Bristol, UK Intelligence augmentation (IA) facilitates a new systems perspective to frame the value outcome of the interaction between human and AI agents. The factors that can optimize this collaborative integration of the multi-agent system are investigated and discussed. Different kinds of knowledge approaches are
met in various contexts to create an optimized IA system in service settings. In this respect, AI agents are not just tools but rather co-creators of value that can influence human agents’ learning cycles. Hence, humans’ effective interaction with AI agents produces a learning effect that can empower humans’ interpretative capability. This chapter focuses on IA and shows that LA is not only a theoretical paradigm but also serves as a platform to facilitate the transition from smart services to wise service innovation to the benefit of both the multi-agent system benefitting service organizations and the consumers too. Potential challenges are also discussed from a societal viewpoint. Chapter 15 Artificial Intelligence in Sports: Monitoring Marathons in Social Media - The Role of Sports Events in Territory Branding. 315 Natalia Vila-Lopez, University of Valencia, Spain Ines Kuster-Boluda, University of Valencia, Spain Francisco J. Sarabia-Sanchez, University Miguel Herndndez, Spain In the sports industry, artificial intelligence has become a powerful tool for sports managers interested in getting private sponsorships and for DMOs interested in branding a place. In this scenario, two main objectives guide this chapter (1) to generate a ranking of the leading Spanish marathons based on their presence on the four most important social networks in Spain (Facebook, Twitter, Instagram, and YouTube) and (2) to measure the engagement on social networks generated by
the first of the marathons identified in the ranking. The official profiles of the accounts of the 10 marathons with the highest number of finishers in 2022 in Spain have been monitored on the social networks listed (Facebook, Twitter, Instagram, and YouTube). As the results show, a marathon can generate high network engagements. The destination’s image can be highly favoured thanks to small local events (such as marathons) capable of generating a lot of movement on social networks. However, not all social networks work equally well in promoting sporting events capable of generating engagement. Chapter 16 AI-Driven Customer Experience: Factors to Consider. 341 Svetlana Bialkova, Liverpool Business School, UK Despite the increasing implementation of artificial intelligence (AI), it is puzzling why consumers are still resistant towards it. Part of the problem is how to create systems that appropriately meet consumer demand for good quality and functional AI. The chapter addresses this issue by providing the muchneeded understanding of how AI technologies can shape a satisfactory customer experience. Results are clear in showing that easy-to-use and high-quality AI systems form positive attitudes, and consumers are willing to use such technology again. Functional and enjoyable interaction enhanced the experience and thus attitude formation. These results have been substantiated statistically only for the high satisfaction group. By contrast, for low satisfaction group, consumers have not enjoyed the
experience they had with the AI system. They found the interaction to be unpleasant, and the system to be useless. The outcomes are summarised in a framework for designing appropriate AI systems shaping consumer journey beyond the traditional marketing context.
Chapter 17 Impact of Artificial Intelligence in Industry 4.0 and 5.0. 358 LuizMotinho, University of Suffolk, UK Luis Cavique, Universidade Aberta, Portugal Industry 4.0 uses the network concept to establish an interconnected manufacturing system. Industry 4.0 integrates the more recent digital concepts such as artificial intelligence (AI), the internet of things (loT), big data, cloud computing, and 3D printing. The next maturity level, Industry 5.0, aims to shift the focus back to human-centric production by creating a sustainable and collaborative environment with humans and machines. Every manufacturer aims to find new ways to increase profits, reduce risks, and improve production efficiency. AI tools can process and interpret vast volumes of data from the production floor to spot patterns, analyze and predict consumer behavior, and detect real-time anomalies in production processes. This work studies the impact of AI in Industries 4.0 and 5.0. In Industry 4.0, AI can help in classic tasks such as predictive maintenance, production optimization, and customer personalization. Industry 5.0 enables sustainable manufacturing development and human-AI interaction. In this work, the authors demonstrate the impact of AI in Industry 4.0 and 5.0. Compilation of References.377 About the
Contributors. 430 Index. 436 |
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Table of Contents Preface. xv Chapter 1 Causality: The Next Step in Artificial Intelligence.1 Luis Cavique, Universidade Aberta, Portugal Chapter 2 Impact of Artificial Intelligence on Marketing Research: Challenges and Ethical Considerations. 18 Laura Sdez-Ortuno, University of Barcelona, Spain Javier Sanchez-Garcia, Jaume I University, Spain Santiago Forgas-Coll, University of Barcelona, Spain Rubén Huertas-Garcia, University of Barcelona, Spain Eloi Puertas-Prat, University of Barcelona, Spain Chapter 3 Artificial Intelligence in Healthcare. 43 Kun-Huang Huarng, National Taipei University of Business, Taiwan Tiffany Hui-Kuang Yu, Feng Chia University, Taiwan Duen-Huang Huang, Chaoyang University of Technology, Taiwan Chapter 4 Causal Machine Learning in Social Impact Assessment. 56 Nuno Castro Lopes, Universidade Aberta, Portugal Luis Cavique, Universidade Aberta, Portugal Chapter 5 The Incorporation of Large Language Models (LLMs) in the Field of Education: Ethical Possibilities, Threats, and Opportunities. Paul Aldrin Pineda Dungca, Salesians of Don Bosco, Philippines 78 Chapter 6 Artificial Intelligence in
Tourism. 98 Enrique Bigné, Universität de Valencia, Spain
Chapter 7 The Influence of Culture on Sentiments Expressed in Online Reviews of Eco-Friendly Hotels: The Case Study of Amsterdam.115 Estefania Ballester Chirica, University of Valencia, Spain Carla Ruiz-Mafé, University of Valencia, Spain Natalia Rubio, Autonomous University of Madrid, Spain Chapter 8 Artificial Intelligence Method for the Analysis of Marketing Scientific Literature. 142 Antonio Hyder, Miguel Hernandez University, Spain Hackers and Founders Research, USA Carlos Perez-Vidal, Universidad Miguel Hernandez, Spain Ronjon Nag, Stanford University, USA Chapter 9 Artificial Intelligence for Renewable Energy Systems and Applications: A Comprehensive Review 160 Manikandakumar Muthusamy, New Horizon College of Engineering, India Karthikeyan Periyasamy, Thiagarajar College of Engineering, India Chapter 10 Exploratory Cluster Analysis Using Self-Organizing Maps: Algorithms, Methodologies, and Framework. 187 Nuno C. Marques, NOVA-LINCS, SST, Universidade NOVA de Lisboa, Portugal Bruno Silva, EST, Polytechnic Institute ofSetubal, Portugal Chapter 11 Persons and Personalization on Digital Platforms: A Philosophical Perspective. 214 Travis Greene, Copenhagen Business School, Denmark Galit Shmueli, National Tsing Hua University, Taiwan Chapter 12 Mind
Uploading in Artificial Intelligence.271 Jason Wissinger, Waynesburg University, USA Elizabeth Baoying Wang, Waynesburg University, USA Chapter 13 Ethical Issues of Artificial Intelligence (AI): Strategic Solutions. 283 Sara Shawky, Griffith University, Australia Park Thaichon, University of Southern Queensland, Australia Sara Quach, Griffith University, Australia Lars-Erik Casper Ferm, The University of Queensland, Australia Chapter 14 Intelligence Augmentation via Human-AI Symbiosis: Formulating Wise Systems for a Metasociety. 301 Nikolaos Stylos, University of Bristol, UK
Chapter 15 Artificial Intelligence in Sports: Monitoring Marathons in Social Media - The Role of Sports Events in Territory Branding. 315 Natalia Vila-Lopez, University of Valencia, Spain Ines Kuster-Boluda, University of Valencia, Spain Francisco J. Sarabia-Sanchez, University Miguel Hernandez, Spain Chapter 16 AI-Driven Customer Experience: Factors to Consider. 341 Svetlana Bialkova, Liverpool Business School, UK Chapter 17 Impact of Artificial Intelligence in Industry 4.0 and 5.0. 358 LuizMotinho, University of Suffolk, UK Luis Cavique, Universidade Aberta, Portugal Compilation of References. 377 About the Contributors. 430 Index. 436
Detailed Table of Contents Preface. xv Chapter 1 Causality: The Next Step in Artificial Intelligence. 1 Luis Cavique, Universidade Aberta, Portugal Judea Pearl’s ladder of causation framework has dramatically influenced the understanding of causality in computer science. Despite artificial intelligence (AI) advancements, grasping causal relationships remains challenging, emphasizing the causal revolution’s significance in improving Al’s understanding of cause and effect. The work presents a novel taxonomy of causal inference methods, clarifying diverse approaches for inferring causality from data. It highlights the implications of causality in responsible AI and explainable AI (xAI), addressing bias in AI systems. The chapter points out causality as the next step in AI for creating new questions, developing causal tools, and clarifying opaque models with xAI approaches. The work clarifies causal models’ significance and implications in various AI subareas. Chapter 2 Impact of Artificial Intelligence on Marketing Research: Challenges and Ethical Considerations. 18 Laura Sdez-Ortufio, University of Barcelona, Spain Javier Sanchez-Garcia, Jaume 1 University, Spain Santiago Forgas-Coll, University of Barcelona, Spain Rubén Huertas-Garcia, University of Barcelona, Spain Eloi Puertas-Prat, University of Barcelona, Spain This chapter
explores the use of artificial intelligence (AI) in market research and its potential impact on the field. Discuss how AI can be used for data collection, filtering, analysis, and prediction, and how it can help companies develop more accurate predictive models and personalized marketing strategies. Highlight the drawbacks of AI, such as the need to ensure diverse and unbiased data and the importance of monitoring and interpreting results and covers various AI techniques used in market research, including machine learning, natural language processing, computer vision, deep learning, and rule-based systems. The applications of AI in marketing research are also discussed, including sentiment analysis, market segmentation, predictive analytics, and adaptive recommendation engines and personalization systems. The chapter concludes that while AI presents many benefits, it also presents several challenges related to data quality and accuracy, algorithmic biases and fairness issues, as well as ethical considerations that need to be carefully considered.
Chapter 3 Artificial Intelligence in Healthcare. 43 Kun-Huang Huarng, National Taipei University of Business, Taiwan Tiffany Hui-Kuang Yu, Feng Chia University, Taiwan Duen-Huang Huang, Chaoyang University of Technology, Taiwan Artificial intelligence (AI) has been applied to various domains to improve the quality of human life. This chapter outlines the recent application of AI in healthcare. A brief history of AI development is first introduced. Machine learning, one of the current AI advancements, is explained. Successful AI application in different areas of healthcare is then showcased, including different medical diagnosis and long-term care. The popular ChatGPT series of systems and their extraordinary performance are described. This chapter ends with debates and future expectations of AI. Chapter 4 Causal Machine Learning in Social Impact Assessment. 56 Nuno Castro Lopes, Universidade Aberta, Portugal Luis Cavique, Universidade Aberta, Portugal Social impact assessment is a fundamental process to verify the achievement of the objectives of interventions and, consequently, to validate investments in the social area. Generally, this process is based on the analysis of the average effects of the intervention, which does not allow a detailed understanding of the individualization of these effects. Causal machine learning methods mark an evolution in causal inference, as they allow for a
more heterogeneous assessment of the effects of interventions. Applying these methods to evaluate the impact of social projects and programs offers the advantage of improving the selection of target audiences and optimizing and personalizing future interventions. In this chapter, in a non-technical way, the authors explore classical causal inference methods to estimate average effects and new causal machine learning methods to evaluate heterogeneous effects. They address adapting the Uplift Modeling method to assess social interventions. They also address the advantages, limitations, and research needs for using these new techniques in social intervention. Chapter 5 The Incorporation of Large Language Models (LLMs) in the Field of Education: Ethical Possibilities, Threats, and Opportunities.78 Paul Aldrin Pineda Dungca, Salesians of Don Bosco, Philippines This chapter delves into the ethical implications that arise from integrating LLMs within the realm of education. LLMs, exemplified by the GPT-3.5, have emerged as formidable instruments for natural language processing, offering diverse applications in educational domains. Nevertheless, their adoption necessitates careful consideration of ethical matters. This chapter comprehensively overviews the ethical potentials, threats, and opportunities in incorporating LLMs into education. It scrutinizes the potential advantages, including enriched personalized learning experiences and enhanced accessibility, while addressing
concerns regarding data privacy, bias, and the ramifications of supplanting human instructors. By critically examining the ethical dimensions, this chapter endeavors to foster a varied comprehension of the implications of utilizing LLMs in educational settings.
Chapter 6 Artificial Intelligence in Tourism. 98 Enrique Bigné, Universität de Valencia, Spain Artificial intelligence in tourism activities opens a bundle of emerging applications for tourists and companies. This chapter aims to delineate the stages of the tourist journey and the usage of four types of intelligence suggested in the literature: mechanical, analytical, intuitive, and empathetic. Based on these two ideas, the authors propose a useful framework for disentangling the different types of current and future applications of AI in tourism. Each stage involves multiple suppliers with different types of AI applications, and its adoption will ultimately rely on tourist trust and, therefore, willingness to share data and the use of robotics and other AI forms. The chapter ends with some trends and reflections on the expansion of AI in tourism that pivot around these ideas: job replacement and flexible operations; mobile-centric approach; data integration and analytics; revenue management and customer interactions tension; (v) neuroscientific tools for AI in tourism. Chapter 7 The Influence of Culture on Sentiments Expressed in Online Reviews of Eco-Friendly Hotels: The Case Study of Amsterdam. 115 Estefania Ballester Chirica, University of Valencia, Spain Carla Ruiz-Mafé, University of Valencia, Spain Natalia Rubio, Autonomous University of
Madrid, Spain The proliferation of content generated by tourists, in parallel with the exponential growth of social media is causing a paradigm shift in research. Traditional surveys cannot be necessary to obtain users’ opinions when scholars can access this valuable information freely through social media. In the domain of tourism, online tourists’ reviews (OTRs) shared on online travel communities stand out. The aim of this study is to demonstrate the usefulness of OTRs in analysing the image of a green hotel. The authors also examine the possible differences in the content of green hotel online reviews across Anglos and European tourists. The data source are 28,189 reviews by tourists shared on TripAdvisor regarding the 82 green hotels of the city of Amsterdam. The findings showed that tourist’s culture significantly determine the content of the OTRs. The results show preferences and opinions from the tourist’s perspective, which can be useful for hotel managers to promoting sustainability practices. Chapter 8 Artificial Intelligence Method for the Analysis of Marketing Scientific Literature. 142 Antonio Hyder, Miguel Hernandez University, Spain Hackers and Founders Research, USA Carlos Perez-Vidal, Universidad Miguel Hernandez, Spain Ronjon Nag, Stanford University, USA A machine-based research reading methodology specific to the academic discipline of marketing science is introduced, focused on the text mining of scientific texts, analysis and predictive writing, by adopting artificial intelligence developments from other research fields
in particular materials and chemical science. It is described how marketing research can be extracted from documents, classified and tokenised in individual words. This is conducted by applying text-mining with named entity recognition together with entity normalisation for large-scale information extraction of published scientific literature. Both a generic methodology for overall marketing science analysis as well as a narrowed-down contextualised method for delimited marketing topics are detailed. Automated literature review is discussed as well
as potential automated formulation of hypotheses and how AI can assist in the transfer of marketing research knowledge to practice, in particular to startups, as they can benefit from AI powered science based decision making. Recommendations for next steps are made. Chapter 9 Artificial Intelligence for Renewable Energy Systems and Applications: A Comprehensive Review 160 Manikandakumar Muthusamy, New Horizon College of Engineering, India Karthikeyan Periyasamy, Thiagarajar College of Engineering, India Artificial Intelligence technology has advanced tremendously in recent years, and it is now widely used in a variety of fields, including energy, agriculture, geology, information processing, medicine, defence systems, space research and exploration, marketing, and many more. The introduction of artificial intelligence technology has ushered in a new era of renewable energy systems and smart power grid modernization. It assists in attaining the intended system availability, reliability, power quality, efficiency, and security goals through optimal resource utilization and cost-effective electricity. Automated power generation systems, energy storage control, wind turbine aerodynamic performance optimization, power generator efficiency enhancement, health monitoring of renewable energy generation systems, and fault detection and diagnose in a smart grid subsystem are just a few of the applications. The main aim of this proposed chapter is to demonstrate how artificial intelligence techniques play a significant role in renewable energy systems with their diverse applications.
Chapter 10 Exploratory Cluster Analysis Using Self-Organizing Maps: Algorithms, Methodologies, and Framework. 187 Nuno C. Marques, NOVA-LINCS, SST, Universidade NOVA de Lisboa, Portugal Bruno Silva, EST, Polytechnic Institute ofSetubal, Portugal As the volume and complexity of data streams continue to increase, exploratory cluster analysis is becoming increasingly important. In this chapter, the authors explore the use of artificial neural networks (ANNs), particularly self-organizing maps (SOMs), for this purpose. They propose additional methodologies, including concept drift detection, as well as distributed and collaborative learning strategies and introduce a new open-source Java ANN library, designed to support practical applications of SOMs across various domains. By following our tutorial, users will gain practical insights into visualizing and analyzing these challenging datasets, enabling them to harness the full potential of our approach in their own projects. Overall, this chapter aims to provide readers with a comprehensive understanding of SOMs and their place within the broader context of artificial neural networks. Furthermore, we offer practical guidance on the effective development and utilization of these models in real-world applications. Chapter 11 Persons and Personalization on Digital Platforms: A Philosophical Perspective. 214 Travis Greene, Copenhagen Business School,
Denmark Galit Shmueli, National Tsing Hua University, Taiwan This chapter explores personalization and its connection to the philosophical concept of the person, arguing that a deeper understanding of the human person and a good society is essential for ethical personalization. Insights from artificial intelligence (AI), philosophy, law, and more are employed to examine personalization technology. The authors present a unified view of personalization as automated
control of human environments through digital platforms and new forms of AI, while also illustrating how platforms can use personalization to control and modify persons’ behavior. The ethical implications of these capabilities are discussed in relation to concepts of personhood to autonomy, privacy, and self-determination within European AI and data protection law. Tentative principles are proposed to better align personalization technology with democratic values, and future trends in personalization for business and public policy are considered. Overall, the chapter seeks to uncover unresolved tensions among philosophical, technological, and economic viewpoints of personalization. Chapter 12 Mind Uploading in Artificial Intelligence.271 Jason Wissinger, Waynesburg University, USA Elizabeth Baoying Wang, Waynesburg University, USA Mind uploading is the futurist idea of emulating all brain processes of an individual on a computer. Progress towards achieving this technology is currently limited by society’s capability to study the human brain and the development of complex artificial neural networks capable of emulating the brain’s architecture. The goal of this chapter is to provide a brief history of both categories, discuss the progress made, and note the roadblocks hindering future research. Then, by examining the roadblocks of neuroscience and artificial intelligence together, this chapter will outline a way to overcome their respective limitations by using the other
field’s strengths. Chapter 13 Ethical Issues of Artificial Intelligence (AI): Strategic Solutions. 283 Sara Shawky, Griffith University, Australia Park Thaichon, University of Southern Queensland, Australia Sara Quach, Griffith University, Australia Lars-Erik Casper Perm, The University of Queensland, Australia Ethical issues of AI have become a huge concern dominating government, media, and academic discourse. This chapter sheds light on some of the most pressing ethical issues that result from the adoption of AIpowered tools. Increasing inequality, widening social and economic gaps, compromising privacy and data protection, outsmarting humans and impacting human rights, lack of accountability, liability and reliability, and lack of empathy and sympathy are considered the most pressing challenges that need to be addressed concerning AI and big data. This chapter also provides insight into strategies that are currently in place to overcome adverse implications of AI in the public and private sectors. Providing insight into these ethical challenges along with the governing solutions makes a significant contribution to the ongoing discourse and urges for bringing forth sustainable solutions that are necessary for the ethical application of these technologies in different fields. Chapter 14 Intelligence Augmentation via Human-AI Symbiosis: Formulating Wise Systems for a
Metasociety. 301 Nikolaos Stylos, University of Bristol, UK Intelligence augmentation (IA) facilitates a new systems perspective to frame the value outcome of the interaction between human and AI agents. The factors that can optimize this collaborative integration of the multi-agent system are investigated and discussed. Different kinds of knowledge approaches are
met in various contexts to create an optimized IA system in service settings. In this respect, AI agents are not just tools but rather co-creators of value that can influence human agents’ learning cycles. Hence, humans’ effective interaction with AI agents produces a learning effect that can empower humans’ interpretative capability. This chapter focuses on IA and shows that LA is not only a theoretical paradigm but also serves as a platform to facilitate the transition from smart services to wise service innovation to the benefit of both the multi-agent system benefitting service organizations and the consumers too. Potential challenges are also discussed from a societal viewpoint. Chapter 15 Artificial Intelligence in Sports: Monitoring Marathons in Social Media - The Role of Sports Events in Territory Branding. 315 Natalia Vila-Lopez, University of Valencia, Spain Ines Kuster-Boluda, University of Valencia, Spain Francisco J. Sarabia-Sanchez, University Miguel Herndndez, Spain In the sports industry, artificial intelligence has become a powerful tool for sports managers interested in getting private sponsorships and for DMOs interested in branding a place. In this scenario, two main objectives guide this chapter (1) to generate a ranking of the leading Spanish marathons based on their presence on the four most important social networks in Spain (Facebook, Twitter, Instagram, and YouTube) and (2) to measure the engagement on social networks generated by
the first of the marathons identified in the ranking. The official profiles of the accounts of the 10 marathons with the highest number of finishers in 2022 in Spain have been monitored on the social networks listed (Facebook, Twitter, Instagram, and YouTube). As the results show, a marathon can generate high network engagements. The destination’s image can be highly favoured thanks to small local events (such as marathons) capable of generating a lot of movement on social networks. However, not all social networks work equally well in promoting sporting events capable of generating engagement. Chapter 16 AI-Driven Customer Experience: Factors to Consider. 341 Svetlana Bialkova, Liverpool Business School, UK Despite the increasing implementation of artificial intelligence (AI), it is puzzling why consumers are still resistant towards it. Part of the problem is how to create systems that appropriately meet consumer demand for good quality and functional AI. The chapter addresses this issue by providing the muchneeded understanding of how AI technologies can shape a satisfactory customer experience. Results are clear in showing that easy-to-use and high-quality AI systems form positive attitudes, and consumers are willing to use such technology again. Functional and enjoyable interaction enhanced the experience and thus attitude formation. These results have been substantiated statistically only for the high satisfaction group. By contrast, for low satisfaction group, consumers have not enjoyed the
experience they had with the AI system. They found the interaction to be unpleasant, and the system to be useless. The outcomes are summarised in a framework for designing appropriate AI systems shaping consumer journey beyond the traditional marketing context.
Chapter 17 Impact of Artificial Intelligence in Industry 4.0 and 5.0. 358 LuizMotinho, University of Suffolk, UK Luis Cavique, Universidade Aberta, Portugal Industry 4.0 uses the network concept to establish an interconnected manufacturing system. Industry 4.0 integrates the more recent digital concepts such as artificial intelligence (AI), the internet of things (loT), big data, cloud computing, and 3D printing. The next maturity level, Industry 5.0, aims to shift the focus back to human-centric production by creating a sustainable and collaborative environment with humans and machines. Every manufacturer aims to find new ways to increase profits, reduce risks, and improve production efficiency. AI tools can process and interpret vast volumes of data from the production floor to spot patterns, analyze and predict consumer behavior, and detect real-time anomalies in production processes. This work studies the impact of AI in Industries 4.0 and 5.0. In Industry 4.0, AI can help in classic tasks such as predictive maintenance, production optimization, and customer personalization. Industry 5.0 enables sustainable manufacturing development and human-AI interaction. In this work, the authors demonstrate the impact of AI in Industry 4.0 and 5.0. Compilation of References.377 About the
Contributors. 430 Index. 436 |
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| id | DE-604.BV049505349 |
| illustrated | Illustrated |
| index_date | 2024-07-03T23:22:13Z |
| indexdate | 2024-07-20T04:35:26Z |
| institution | BVB |
| isbn | 9781668495926 9781668495919 |
| language | English |
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| physical | xxii, 439 Seiten Illustrationen 279 mm. |
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| publisher | IGI Global |
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| series2 | Advances in human and social aspects of technology (AHSAT) book series Premier reference source |
| spelling | Moutinho, Luiz 1949- Verfasser (DE-588)131450204 aut Philosophy of artificial intelligence and its place in society Luiz Moutinho (University of Suffolk, UK), Luís Cavique (Universidade Aberta, Portugal), Enrique Bigné (Universitat de València, Spain) Hershey, PA IGI Global [2023] xxii, 439 Seiten Illustrationen 279 mm. txt rdacontent n rdamedia nc rdacarrier Advances in human and social aspects of technology (AHSAT) book series Premier reference source Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Ethik (DE-588)4015602-3 gnd rswk-swf Tierethik (DE-588)4194901-8 gnd rswk-swf Bioethik (DE-588)4006791-9 gnd rswk-swf Künstliche Intelligenz Ethik, Moralphilosophie Bioethik, Tierethik Künstliche Intelligenz (DE-588)4033447-8 s Ethik (DE-588)4015602-3 s Bioethik (DE-588)4006791-9 s Tierethik (DE-588)4194901-8 s DE-604 Cavique, Luis ca. 20./21. Jh. Verfasser (DE-588)1324410787 aut Bigné Alcañiz, J. Enrique Verfasser (DE-588)171245032 aut Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034850413&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Moutinho, Luiz 1949- Cavique, Luis ca. 20./21. Jh Bigné Alcañiz, J. Enrique Philosophy of artificial intelligence and its place in society Künstliche Intelligenz (DE-588)4033447-8 gnd Ethik (DE-588)4015602-3 gnd Tierethik (DE-588)4194901-8 gnd Bioethik (DE-588)4006791-9 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4015602-3 (DE-588)4194901-8 (DE-588)4006791-9 |
| title | Philosophy of artificial intelligence and its place in society |
| title_auth | Philosophy of artificial intelligence and its place in society |
| title_exact_search | Philosophy of artificial intelligence and its place in society |
| title_exact_search_txtP | Philosophy of artificial intelligence and its place in society |
| title_full | Philosophy of artificial intelligence and its place in society Luiz Moutinho (University of Suffolk, UK), Luís Cavique (Universidade Aberta, Portugal), Enrique Bigné (Universitat de València, Spain) |
| title_fullStr | Philosophy of artificial intelligence and its place in society Luiz Moutinho (University of Suffolk, UK), Luís Cavique (Universidade Aberta, Portugal), Enrique Bigné (Universitat de València, Spain) |
| title_full_unstemmed | Philosophy of artificial intelligence and its place in society Luiz Moutinho (University of Suffolk, UK), Luís Cavique (Universidade Aberta, Portugal), Enrique Bigné (Universitat de València, Spain) |
| title_short | Philosophy of artificial intelligence and its place in society |
| title_sort | philosophy of artificial intelligence and its place in society |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd Ethik (DE-588)4015602-3 gnd Tierethik (DE-588)4194901-8 gnd Bioethik (DE-588)4006791-9 gnd |
| topic_facet | Künstliche Intelligenz Ethik Tierethik Bioethik |
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