Pub Date : 2025-02-28DOI: 10.1109/TTS.2024.3467797
{"title":"IEEE Transactions on Technology and Society Publication Information","authors":"","doi":"10.1109/TTS.2024.3467797","DOIUrl":"https://doi.org/10.1109/TTS.2024.3467797","url":null,"abstract":"","PeriodicalId":73324,"journal":{"name":"IEEE transactions on technology and society","volume":"6 1","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908496","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-25DOI: 10.1109/TTS.2025.3540978
Armin Alimardani
This empirical study examines the outcomes of integrating Generative AI (GenAI) into a law assignment at the School of Law, University of Wollongong, Australia. Despite receiving instructions on the importance of verifying GenAI outputs and feedback on their attempts to use these tools effectively, a notable portion of students included fabricated or inaccurate information that had been generated by AI in their assignments. This overreliance on AI outputs suggests that instruction and guided practice alone may not sufficiently mitigate the risks associated with the inappropriate use of GenAI. A particularly concerning issue is the difficulty of identifying AI-generated inaccuracies in assessment tasks, which often requires considerable time and effort. Consequently, such errors may go unnoticed, potentially allowing students to bypass the development of essential skills, such as critical thinking and the ability to independently evaluate the accuracy, credibility, and relevance of information. Addressing overreliance on GenAI will require developing robust strategies that should be implemented for the entire duration of a student’s university degree to ensure they engage with AI tools effectively and responsibly.
{"title":"Borderline Disaster: An Empirical Study on Student Usage of GenAI in a Law Assignment","authors":"Armin Alimardani","doi":"10.1109/TTS.2025.3540978","DOIUrl":"https://doi.org/10.1109/TTS.2025.3540978","url":null,"abstract":"This empirical study examines the outcomes of integrating Generative AI (GenAI) into a law assignment at the School of Law, University of Wollongong, Australia. Despite receiving instructions on the importance of verifying GenAI outputs and feedback on their attempts to use these tools effectively, a notable portion of students included fabricated or inaccurate information that had been generated by AI in their assignments. This overreliance on AI outputs suggests that instruction and guided practice alone may not sufficiently mitigate the risks associated with the inappropriate use of GenAI. A particularly concerning issue is the difficulty of identifying AI-generated inaccuracies in assessment tasks, which often requires considerable time and effort. Consequently, such errors may go unnoticed, potentially allowing students to bypass the development of essential skills, such as critical thinking and the ability to independently evaluate the accuracy, credibility, and relevance of information. Addressing overreliance on GenAI will require developing robust strategies that should be implemented for the entire duration of a student’s university degree to ensure they engage with AI tools effectively and responsibly.","PeriodicalId":73324,"journal":{"name":"IEEE transactions on technology and society","volume":"6 2","pages":"210-219"},"PeriodicalIF":0.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid rise of generative AI reshapes society, transforming jobs, relationships, and core beliefs about human essence. AI’s ability to simulate empathy, once considered uniquely human, offers promise in industries from marketing to healthcare but also risks exploiting emotional vulnerabilities, fostering dependency, and compromising privacy. These risks are particularly acute with AI companion chatbots, which mimic emotional speech but may erode genuine human connections. Rooted in Schopenhauer’s compassionate imperative, we present a novel framework for compassionate AI design, governance, and use, emphasizing equitable distribution of AI’s benefits and burdens based on stakeholder vulnerability. We advocate for responsible AI development that prioritizes empathy, dignity, and human flourishing.
{"title":"Compassionate AI Design, Governance, and Use","authors":"Raffaele Fabio Ciriello;Angelina Ying Chen;Zara Annette Rubinsztein","doi":"10.1109/TTS.2025.3538125","DOIUrl":"https://doi.org/10.1109/TTS.2025.3538125","url":null,"abstract":"The rapid rise of generative AI reshapes society, transforming jobs, relationships, and core beliefs about human essence. AI’s ability to simulate empathy, once considered uniquely human, offers promise in industries from marketing to healthcare but also risks exploiting emotional vulnerabilities, fostering dependency, and compromising privacy. These risks are particularly acute with AI companion chatbots, which mimic emotional speech but may erode genuine human connections. Rooted in Schopenhauer’s compassionate imperative, we present a novel framework for compassionate AI design, governance, and use, emphasizing equitable distribution of AI’s benefits and burdens based on stakeholder vulnerability. We advocate for responsible AI development that prioritizes empathy, dignity, and human flourishing.","PeriodicalId":73324,"journal":{"name":"IEEE transactions on technology and society","volume":"6 3","pages":"270-275"},"PeriodicalIF":0.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10885533","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-10DOI: 10.1109/TTS.2025.3527897
Michael Goodchild;Gary M. Langham;Richard Appelbaum;Jeremy Crampton;William A. Herbert;Krzysztof Janowicz;Mei-Po Kwan;Katina Michael;Lisa Schamess
This article presents a paper developed by the AAG Organizing Committee on Locational Information and the Public Interest through a summit held in Santa Barbara, California in June 2022. The summit resulted in goals and ideas for addressing the issues that arise from the present environment for geodata, whereby public, private, and third-sector entities can tap into publicly available locational information with relatively little regulation on its access or use. The Committee articulates four goals: (1) develop a research agenda extending across disciplines, (2) outline educational resources and strategies to guide ethical practice, (3) devise a pathway to increase public understanding, and (4) create a path to increased dialogue with non-traditional and indirect stakeholders in GIS, as well as increased collaboration between academic, public, and private sectors on the use of locational information. These goals were developed to highlight the host of ethical issues that may arise in the use of locational data, whether for research, commercial application, public administration, communities or any other purpose. Some of the ethical issues are specific to this type of data and will not arise over the use of data that are not locational. Others are common to data sampling, in general, regardless of whether location is collected or not. For this project, the focus is on those issues that arise only when the data are locational. GeoEthics is the term used by this Committee that incorporates geoprivacy among other concepts.
{"title":"Locational Data and the Public Interest","authors":"Michael Goodchild;Gary M. Langham;Richard Appelbaum;Jeremy Crampton;William A. Herbert;Krzysztof Janowicz;Mei-Po Kwan;Katina Michael;Lisa Schamess","doi":"10.1109/TTS.2025.3527897","DOIUrl":"https://doi.org/10.1109/TTS.2025.3527897","url":null,"abstract":"This article presents a paper developed by the AAG Organizing Committee on Locational Information and the Public Interest through a summit held in Santa Barbara, California in June 2022. The summit resulted in goals and ideas for addressing the issues that arise from the present environment for geodata, whereby public, private, and third-sector entities can tap into publicly available locational information with relatively little regulation on its access or use. The Committee articulates four goals: (1) develop a research agenda extending across disciplines, (2) outline educational resources and strategies to guide ethical practice, (3) devise a pathway to increase public understanding, and (4) create a path to increased dialogue with non-traditional and indirect stakeholders in GIS, as well as increased collaboration between academic, public, and private sectors on the use of locational information. These goals were developed to highlight the host of ethical issues that may arise in the use of locational data, whether for research, commercial application, public administration, communities or any other purpose. Some of the ethical issues are specific to this type of data and will not arise over the use of data that are not locational. Others are common to data sampling, in general, regardless of whether location is collected or not. For this project, the focus is on those issues that arise only when the data are locational. GeoEthics is the term used by this Committee that incorporates geoprivacy among other concepts.","PeriodicalId":73324,"journal":{"name":"IEEE transactions on technology and society","volume":"6 2","pages":"129-154"},"PeriodicalIF":0.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10879120","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-03DOI: 10.1109/TTS.2025.3531780
Corey M. Hartman;Bhaskar P. Rimal
Recent studies have found that 43% of malware infections begin as malicious Microsoft Office documents in the form of Word or Excel file. While many techniques are proposed and are effective in the detection of malicious documents through the utilization of machine learning (ML) algorithms, bias in the datasets and the lack of insight into the decision as to why a document was flagged as malicious are problematic, as one key feature focused on by the ML model utilized may be relied on solely for the prediction that is made. By utilizing the SHAP algorithm (SHapley Additive exPlanation) and an ensemble of ML algorithms split into groups by their SHAP magnitude, where those features taking over the decision-making process of a model are split into their own feature set and are utilized in the training of a separate ML model, a voting classifier can be made to reduce this bias and reliance on a single or select few features. That allows for a more robust ML model for predicting malicious Office documents and presenting more insight into why a prediction was made by the classifier and a model that can let the user know when not enough data is present to predict with confidence. By utilizing this technique, an ensemble soft voting classifier was created that obtained 90.1% accuracy on a balanced dataset consisting of 250 malicious and 250 benign randomly selected Office documents and presents the user with a simple natural language statement that indicates the classification of the documents and why it was classified as a specific label.
{"title":"Interpretable Machine Learning for Mitigating Feature-Driven Attacks","authors":"Corey M. Hartman;Bhaskar P. Rimal","doi":"10.1109/TTS.2025.3531780","DOIUrl":"https://doi.org/10.1109/TTS.2025.3531780","url":null,"abstract":"Recent studies have found that 43% of malware infections begin as malicious Microsoft Office documents in the form of Word or Excel file. While many techniques are proposed and are effective in the detection of malicious documents through the utilization of machine learning (ML) algorithms, bias in the datasets and the lack of insight into the decision as to why a document was flagged as malicious are problematic, as one key feature focused on by the ML model utilized may be relied on solely for the prediction that is made. By utilizing the SHAP algorithm (SHapley Additive exPlanation) and an ensemble of ML algorithms split into groups by their SHAP magnitude, where those features taking over the decision-making process of a model are split into their own feature set and are utilized in the training of a separate ML model, a voting classifier can be made to reduce this bias and reliance on a single or select few features. That allows for a more robust ML model for predicting malicious Office documents and presenting more insight into why a prediction was made by the classifier and a model that can let the user know when not enough data is present to predict with confidence. By utilizing this technique, an ensemble soft voting classifier was created that obtained 90.1% accuracy on a balanced dataset consisting of 250 malicious and 250 benign randomly selected Office documents and presents the user with a simple natural language statement that indicates the classification of the documents and why it was classified as a specific label.","PeriodicalId":73324,"journal":{"name":"IEEE transactions on technology and society","volume":"6 2","pages":"220-230"},"PeriodicalIF":0.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-17DOI: 10.1109/TTS.2024.3521341
Jordan Tschida;Katina Michael;Troy McDaniel
Loneliness and social isolation are prevalent among older adults and are associated with adverse health outcomes. Social robots offer a novel approach to addressing these issues by providing companionship and social support. This study examines the preferences of older adults when conversing with social robots that use verbal and nonverbal communications. The methodology of this study incorporated both quantitative and qualitative approaches. Sixteen older adults residing in an independent living facility participated in a 4-week study, during which they were observed interacting with a social robot in weekly sessions. The study employed a Wizard-of-Oz experimental design to investigate verbal and nonverbal communication levels. Data was collected in three phases beginning with the human-to-robot conversation observation immediately followed by a post-interaction survey, open-ended interviews, and finally a post-experience survey. Participants reported positive experiences with the robot, including companionship, enjoyment, and emotional support. The robot’s ability to remember details about participants and engage in responsive conversation was highly valued. All participants desired the robot to have more verbal and nonverbal communication skills. The preliminary findings suggest that social robots have the potential to mitigate loneliness and enhance social connectedness among older adults. Further research with more diverse samples is warranted to validate these findings and explore long-term effects. Addressing ethical considerations will be crucial to maximize the benefits of social robots in promoting the well-being of aging populations.
{"title":"Exploring Social Robots for Healthy Older Adults: Aging With Companionship","authors":"Jordan Tschida;Katina Michael;Troy McDaniel","doi":"10.1109/TTS.2024.3521341","DOIUrl":"https://doi.org/10.1109/TTS.2024.3521341","url":null,"abstract":"Loneliness and social isolation are prevalent among older adults and are associated with adverse health outcomes. Social robots offer a novel approach to addressing these issues by providing companionship and social support. This study examines the preferences of older adults when conversing with social robots that use verbal and nonverbal communications. The methodology of this study incorporated both quantitative and qualitative approaches. Sixteen older adults residing in an independent living facility participated in a 4-week study, during which they were observed interacting with a social robot in weekly sessions. The study employed a Wizard-of-Oz experimental design to investigate verbal and nonverbal communication levels. Data was collected in three phases beginning with the human-to-robot conversation observation immediately followed by a post-interaction survey, open-ended interviews, and finally a post-experience survey. Participants reported positive experiences with the robot, including companionship, enjoyment, and emotional support. The robot’s ability to remember details about participants and engage in responsive conversation was highly valued. All participants desired the robot to have more verbal and nonverbal communication skills. The preliminary findings suggest that social robots have the potential to mitigate loneliness and enhance social connectedness among older adults. Further research with more diverse samples is warranted to validate these findings and explore long-term effects. Addressing ethical considerations will be crucial to maximize the benefits of social robots in promoting the well-being of aging populations.","PeriodicalId":73324,"journal":{"name":"IEEE transactions on technology and society","volume":"6 3","pages":"257-269"},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-24DOI: 10.1109/TTS.2024.3513777
Leonardo Gabrielli;Emanuele Principi;Luca Turchet
The Internet of Sounds (IoS) is an emerging field promoted by a large network of research institutions and companies, which fosters research and new industrial and civil applications in domains such as audio processing, music performance, entertainment and environmental monitoring. Being based on novel technologies and computing paradigms, its environmental costs are yet to be assessed. In previous works, the foundations for an environmental impact assessment in the field were laid down. In this paper, a methodology is built based on an extensive literature survey to identify the foremost emission drivers in the IoS, and apply the collected knowledge to the qualitative analysis of five relevant case studies in the IoS that the authors identify. These are identified from the IoS literature in order to cover two orthogonal axes: artistic-functional and emerging-mature. Their discussion allows a qualitative prediction of their impact, which is positive in two over five cases, negative in the other two and very low in the last one. Considerations, design tips, social suggestions, and future challenges are also outlined.
{"title":"Sustainability and the Internet of Sounds: Case Studies","authors":"Leonardo Gabrielli;Emanuele Principi;Luca Turchet","doi":"10.1109/TTS.2024.3513777","DOIUrl":"https://doi.org/10.1109/TTS.2024.3513777","url":null,"abstract":"The Internet of Sounds (IoS) is an emerging field promoted by a large network of research institutions and companies, which fosters research and new industrial and civil applications in domains such as audio processing, music performance, entertainment and environmental monitoring. Being based on novel technologies and computing paradigms, its environmental costs are yet to be assessed. In previous works, the foundations for an environmental impact assessment in the field were laid down. In this paper, a methodology is built based on an extensive literature survey to identify the foremost emission drivers in the IoS, and apply the collected knowledge to the qualitative analysis of five relevant case studies in the IoS that the authors identify. These are identified from the IoS literature in order to cover two orthogonal axes: artistic-functional and emerging-mature. Their discussion allows a qualitative prediction of their impact, which is positive in two over five cases, negative in the other two and very low in the last one. Considerations, design tips, social suggestions, and future challenges are also outlined.","PeriodicalId":73324,"journal":{"name":"IEEE transactions on technology and society","volume":"6 2","pages":"165-180"},"PeriodicalIF":0.0,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10813621","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent trends in the modus operandi of technologically-aware criminal groups engaged in illicit goods trafficking (e.g., firearms, drugs, cultural artifacts, etc.) have given rise to significant security challenges. The use of cryptocurrency-based payments, 3D printing, social media and/or the Dark Web by organized crime leads to transactions beyond the reach of authorities, thus opening up new business opportunities to criminal actors at the expense of the greater societal good and the rule of law. As a result, a lot of scientific effort has been expended on handling these challenges, with Artificial Intelligence (AI) at the forefront of this quest, mostly machine learning and data mining methods that can automate large-scale information analysis. Deep Neural Networks (DNNs) and graph analytics have been employed to automatically monitor and analyze the digital activities of large criminal networks in a data-driven manner. However, such practices unavoidably give rise to ethical and legal issues, which need to be properly considered and addressed. This paper is the first to explore these aspects jointly, without focusing on a particular angle or type of illicit goods trafficking. It emphasizes how advances in AI both allow the authorities to unravel technologically-aware trafficking networks and provide countermeasures against any potential violations of citizens’ rights in the name of security.
{"title":"The Invisible Arms Race: Digital Trends in Illicit Goods Trafficking and AI-Enabled Responses","authors":"Ioannis Mademlis;Marina Mancuso;Caterina Paternoster;Spyridon Evangelatos;Emma Finlay;Joshua Hughes;Panagiotis Radoglou-Grammatikis;Panagiotis Sarigiannidis;Georgios Stavropoulos;Konstantinos Votis;Georgios Th. Papadopoulos","doi":"10.1109/TTS.2024.3514683","DOIUrl":"https://doi.org/10.1109/TTS.2024.3514683","url":null,"abstract":"Recent trends in the modus operandi of technologically-aware criminal groups engaged in illicit goods trafficking (e.g., firearms, drugs, cultural artifacts, etc.) have given rise to significant security challenges. The use of cryptocurrency-based payments, 3D printing, social media and/or the Dark Web by organized crime leads to transactions beyond the reach of authorities, thus opening up new business opportunities to criminal actors at the expense of the greater societal good and the rule of law. As a result, a lot of scientific effort has been expended on handling these challenges, with Artificial Intelligence (AI) at the forefront of this quest, mostly machine learning and data mining methods that can automate large-scale information analysis. Deep Neural Networks (DNNs) and graph analytics have been employed to automatically monitor and analyze the digital activities of large criminal networks in a data-driven manner. However, such practices unavoidably give rise to ethical and legal issues, which need to be properly considered and addressed. This paper is the first to explore these aspects jointly, without focusing on a particular angle or type of illicit goods trafficking. It emphasizes how advances in AI both allow the authorities to unravel technologically-aware trafficking networks and provide countermeasures against any potential violations of citizens’ rights in the name of security.","PeriodicalId":73324,"journal":{"name":"IEEE transactions on technology and society","volume":"6 2","pages":"181-199"},"PeriodicalIF":0.0,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-17DOI: 10.1109/TTS.2024.3513775
Szu-Yu Kuo;Liang-Bi Chen
Due to the continuous development of globalization and the new era of the shipping industry, the smartening of port operations has become an urgent need. However, the question of whether smart ports impact the safety of port operations is worth exploring. This study examines the relationships among emotional intelligence, artificial intelligence, and safety behavior in the context of container terminal operations. This study drew upon the levels of control framework and conservation of resources theory to examine survey data collected from 281 operators working at Kaohsiung port. The survey responses were evaluated using confirmatory factor analysis and a hierarchical regression model. The findings indicate that emotional and artificial intelligence significantly impact safety behavior. Furthermore, interactive self-emotion appraisal and emotion regulation positively moderate safety compliance. This study offers novel insights pertaining to terminal operations and discusses ways of employing artificial intelligence to facilitate safety operations at container terminals.
{"title":"Utilizing Emotional Intelligence and Artificial Intelligence to Improve Safety Behavior in Smart Port Operations","authors":"Szu-Yu Kuo;Liang-Bi Chen","doi":"10.1109/TTS.2024.3513775","DOIUrl":"https://doi.org/10.1109/TTS.2024.3513775","url":null,"abstract":"Due to the continuous development of globalization and the new era of the shipping industry, the smartening of port operations has become an urgent need. However, the question of whether smart ports impact the safety of port operations is worth exploring. This study examines the relationships among emotional intelligence, artificial intelligence, and safety behavior in the context of container terminal operations. This study drew upon the levels of control framework and conservation of resources theory to examine survey data collected from 281 operators working at Kaohsiung port. The survey responses were evaluated using confirmatory factor analysis and a hierarchical regression model. The findings indicate that emotional and artificial intelligence significantly impact safety behavior. Furthermore, interactive self-emotion appraisal and emotion regulation positively moderate safety compliance. This study offers novel insights pertaining to terminal operations and discusses ways of employing artificial intelligence to facilitate safety operations at container terminals.","PeriodicalId":73324,"journal":{"name":"IEEE transactions on technology and society","volume":"6 3","pages":"283-294"},"PeriodicalIF":0.0,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-13DOI: 10.1109/TTS.2024.3476041
Heather A. Love;Greg Adamson;Mallory James;Jason Lajoie;Iven Mareels;Zach Pearl;Daniel S. Schiff;Ketra Schmitt;Thirumala Arohi;John Buchanan;Stéphanie Camaréna;Marten Kaevats;Jeremy Reynolds;Pedro H. Albuquerque;John C. Havens;Davis Chacón-Hurtado;Sucheta Lahiri;Ayse Ocal;Alexi Orchard;Michael Rigby;Rebecca Sherlock;Victor Sundararaj;Qin Zhu
This article synthesizes the insights gained through presentations and discussions at the 2023 IEEE Workshop on Norbert Wiener in the 21st Century (21CW2023), which focused on “The Future of Work in the Age of Automation.” Hosted at Purdue University, this interdisciplinary convening of technologists, social scientists, and humanists explored the impacts of automation on labor, drawing on Wiener’s legacy of insights as a backdrop to examine the technologically mediated future we face in coming decades. The workshop presented a rare opportunity to reflect critically on these issues at a pivotal moment in human and technological history, and to elicit underappreciated dimensions. Areas of focus include: the qualitative and quantitative losses associated with automation and AI, the impacts automation has for questions about the meaningfulness of work, the challenges we face related to uncertainty and lack of predictability in technological advancement, and the opportunities that exist for centering human values and agency in these conversations. While acknowledging many items for concern in the context of automation in the future of work, such as the domination of economic narratives, a potential loss of qualitative texture, and the neglect of certain issues key to human identity, the authors conclude by offering optimistic visions—or calls—for redefining value and labor, preserving human agency, and embracing creative problem-solving.
{"title":"The Future of Work in the Age of Automation: Proceedings of a Workshop on Norbert Wiener’s 21st Century Legacy","authors":"Heather A. Love;Greg Adamson;Mallory James;Jason Lajoie;Iven Mareels;Zach Pearl;Daniel S. Schiff;Ketra Schmitt;Thirumala Arohi;John Buchanan;Stéphanie Camaréna;Marten Kaevats;Jeremy Reynolds;Pedro H. Albuquerque;John C. Havens;Davis Chacón-Hurtado;Sucheta Lahiri;Ayse Ocal;Alexi Orchard;Michael Rigby;Rebecca Sherlock;Victor Sundararaj;Qin Zhu","doi":"10.1109/TTS.2024.3476041","DOIUrl":"https://doi.org/10.1109/TTS.2024.3476041","url":null,"abstract":"This article synthesizes the insights gained through presentations and discussions at the 2023 IEEE Workshop on Norbert Wiener in the 21st Century (21CW2023), which focused on “The Future of Work in the Age of Automation.” Hosted at Purdue University, this interdisciplinary convening of technologists, social scientists, and humanists explored the impacts of automation on labor, drawing on Wiener’s legacy of insights as a backdrop to examine the technologically mediated future we face in coming decades. The workshop presented a rare opportunity to reflect critically on these issues at a pivotal moment in human and technological history, and to elicit underappreciated dimensions. Areas of focus include: the qualitative and quantitative losses associated with automation and AI, the impacts automation has for questions about the meaningfulness of work, the challenges we face related to uncertainty and lack of predictability in technological advancement, and the opportunities that exist for centering human values and agency in these conversations. While acknowledging many items for concern in the context of automation in the future of work, such as the domination of economic narratives, a potential loss of qualitative texture, and the neglect of certain issues key to human identity, the authors conclude by offering optimistic visions—or calls—for redefining value and labor, preserving human agency, and embracing creative problem-solving.","PeriodicalId":73324,"journal":{"name":"IEEE transactions on technology and society","volume":"6 4","pages":"430-452"},"PeriodicalIF":0.0,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10798994","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145814497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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