1.
Press, Alex
How the UṠ. Labor Movement Is Confronting AI Journal Article
In: New Labor Forum, vol. 33, no. 3, pp. 15–22, 2024, ISSN: 1095-7960, (Publisher: SAGE Publications Inc).
@article{press_how_2024,
title = {How the UṠ. Labor Movement Is Confronting AI},
author = {Alex Press},
url = {https://doi.org/10.1177/10957960241276516},
doi = {10.1177/10957960241276516},
issn = {1095-7960},
year = {2024},
date = {2024-09-01},
urldate = {2024-11-24},
journal = {New Labor Forum},
volume = {33},
number = {3},
pages = {15–22},
abstract = {When Boston University graduate students went on strike in April, Stan Sclaroff, the university’s dean of arts and sciences, sent faculty an email with suggestions for keeping their classes on track. As Inside Higher Ed reported, the dean’s “creative” solutions included combining discussion sections, alternative assignments, and using “generative AI tools like ChatGPT.” Professors, the dean wrote, could use the technology to “give feedback or facilitate ‘discussion’ on readings or assignments.”
Sclaroff’s suggestion that artificial intelligence (AI) could handle struck work provoked an outcry from grad students and faculty, with Service Employees International Union (SEIU) Local 509, which represents the grad students, stating, “We are extremely disappointed by the university’s suggestion that the use of AI could even begin to substitute the hard work that graduate workers pour into mentoring students, facilitating discussions and teaching.” The union told Inside Higher Ed that they hoped the administration would reconsider the suggestion and instead “focus on properly compensating the people who do the work that is crucial in keeping the university running.” The university soon backtracked, claiming, “Neither Dean Sclaroff nor Boston University believe that AI can replace its graduate student teaching assistants, and the assertion that we plan to do so is patently false.”
Since ChatGPT launched in November 2022, educators are just one of many workers now confronted with emerging technology wielded by management as a threat. Across the United States, AI has occasioned all manner of anxieties. It’s not just AI’s environmental impacts, discriminatory biases, or privacy risks: according to one study…},
note = {Publisher: SAGE Publications Inc},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
When Boston University graduate students went on strike in April, Stan Sclaroff, the university’s dean of arts and sciences, sent faculty an email with suggestions for keeping their classes on track. As Inside Higher Ed reported, the dean’s “creative” solutions included combining discussion sections, alternative assignments, and using “generative AI tools like ChatGPT.” Professors, the dean wrote, could use the technology to “give feedback or facilitate ‘discussion’ on readings or assignments.”
Sclaroff’s suggestion that artificial intelligence (AI) could handle struck work provoked an outcry from grad students and faculty, with Service Employees International Union (SEIU) Local 509, which represents the grad students, stating, “We are extremely disappointed by the university’s suggestion that the use of AI could even begin to substitute the hard work that graduate workers pour into mentoring students, facilitating discussions and teaching.” The union told Inside Higher Ed that they hoped the administration would reconsider the suggestion and instead “focus on properly compensating the people who do the work that is crucial in keeping the university running.” The university soon backtracked, claiming, “Neither Dean Sclaroff nor Boston University believe that AI can replace its graduate student teaching assistants, and the assertion that we plan to do so is patently false.”
Since ChatGPT launched in November 2022, educators are just one of many workers now confronted with emerging technology wielded by management as a threat. Across the United States, AI has occasioned all manner of anxieties. It’s not just AI’s environmental impacts, discriminatory biases, or privacy risks: according to one study…
Sclaroff’s suggestion that artificial intelligence (AI) could handle struck work provoked an outcry from grad students and faculty, with Service Employees International Union (SEIU) Local 509, which represents the grad students, stating, “We are extremely disappointed by the university’s suggestion that the use of AI could even begin to substitute the hard work that graduate workers pour into mentoring students, facilitating discussions and teaching.” The union told Inside Higher Ed that they hoped the administration would reconsider the suggestion and instead “focus on properly compensating the people who do the work that is crucial in keeping the university running.” The university soon backtracked, claiming, “Neither Dean Sclaroff nor Boston University believe that AI can replace its graduate student teaching assistants, and the assertion that we plan to do so is patently false.”
Since ChatGPT launched in November 2022, educators are just one of many workers now confronted with emerging technology wielded by management as a threat. Across the United States, AI has occasioned all manner of anxieties. It’s not just AI’s environmental impacts, discriminatory biases, or privacy risks: according to one study…
2.
Song, Keni; Guo, Ming; Ye, Long; Liu, Yunshuo; Liu, Shuzhen
Driverless metros are coming, but what about the drivers? A study on AI-related anxiety and safety performance Journal Article
In: Safety Science, vol. 175, pp. 106487, 2024, ISSN: 0925-7535.
@article{song_driverless_2024,
title = {Driverless metros are coming, but what about the drivers? A study on AI-related anxiety and safety performance},
author = {Keni Song and Ming Guo and Long Ye and Yunshuo Liu and Shuzhen Liu},
url = {https://www.sciencedirect.com/science/article/pii/S0925753524000778},
doi = {10.1016/j.ssci.2024.106487},
issn = {0925-7535},
year = {2024},
date = {2024-07-01},
urldate = {2024-11-24},
journal = {Safety Science},
volume = {175},
pages = {106487},
abstract = {The application of automatic train operation (ATO) systems in rail transit gradually replaces manual labor, greatly impacting metro drivers’ daily work. Building on the cognitive appraisal theory of stress and the socially embedded model of thriving at work, this study explored how drivers’ perception of ATO systems would affect their safety behavior within the workplace, in other words, the psychological and behavioral costs of drivers’ AI-related anxiety. Specifically, we analyzed whether job insecurity and thriving at work would mediate the association between AI-related anxiety and safety performance. We also examined the potential attenuating effects of Human Resource (HR) system strength on AI-related anxiety’s impact on job insecurity, thriving at work, and safety performance. We gathered 364 sets of paired data from Chinese metro drivers and their direct supervisors based on a time-lagged design. Moderated serial mediation analyses revealed that AI-related anxiety exerted a significant serial indirect impact on safety performance via job insecurity and thriving at work. Additionally, we found conditional serial indirect effects of AI-related anxiety on safety performance as a function of drivers’ perceptions of HR system strength. Theoretical and practical implications are discussed for a scholarly and practical understanding of dealing with AI-related anxiety. Metro organizations were recommended to place more emphasis on the HR process to mitigate the costs of drivers’ AI-related anxiety.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The application of automatic train operation (ATO) systems in rail transit gradually replaces manual labor, greatly impacting metro drivers’ daily work. Building on the cognitive appraisal theory of stress and the socially embedded model of thriving at work, this study explored how drivers’ perception of ATO systems would affect their safety behavior within the workplace, in other words, the psychological and behavioral costs of drivers’ AI-related anxiety. Specifically, we analyzed whether job insecurity and thriving at work would mediate the association between AI-related anxiety and safety performance. We also examined the potential attenuating effects of Human Resource (HR) system strength on AI-related anxiety’s impact on job insecurity, thriving at work, and safety performance. We gathered 364 sets of paired data from Chinese metro drivers and their direct supervisors based on a time-lagged design. Moderated serial mediation analyses revealed that AI-related anxiety exerted a significant serial indirect impact on safety performance via job insecurity and thriving at work. Additionally, we found conditional serial indirect effects of AI-related anxiety on safety performance as a function of drivers’ perceptions of HR system strength. Theoretical and practical implications are discussed for a scholarly and practical understanding of dealing with AI-related anxiety. Metro organizations were recommended to place more emphasis on the HR process to mitigate the costs of drivers’ AI-related anxiety.
3.
Mustapha, Zakari; Tieru, Chris Kurbom; Akomah, Benjamin Boahene; Yankah, Jonas Ekow
Limitations for the Implementation of Artificial Intelligence in Construction Health and Safety in Ghana Journal Article
In: Baltic Journal of Real Estate Economics and Construction Management, vol. 12, no. 1, pp. 103–118, 2024.
@article{mustapha_limitations_2024,
title = {Limitations for the Implementation of Artificial Intelligence in Construction Health and Safety in Ghana},
author = {Zakari Mustapha and Chris Kurbom Tieru and Benjamin Boahene Akomah and Jonas Ekow Yankah},
url = {https://sciendo.com/article/10.2478/bjreecm-2024-0007},
doi = {10.2478/bjreecm-2024-0007},
year = {2024},
date = {2024-07-01},
urldate = {2024-11-24},
journal = {Baltic Journal of Real Estate Economics and Construction Management},
volume = {12},
number = {1},
pages = {103–118},
abstract = {Building accidents and fatalities are prevalent, especially in rising nations like Ghana, despite rapid technical developments. Weak regulations, training, and change resistance typically undermine traditional safety measures. This study aimed to identify potential obstacles that prevent the implementation of artificial intelligence (AI) in construction health and safety in Ghana. A survey research approach was employed to get the study population, which consisted of 110 construction experts made up of project managers, site engineers, skilled workers, and safety officers complete the questionnaire. Data analysis included descriptive statistics, chi-square, and regression. According to varied demographic responses, AI increases design and engineering, safety and security, and human resources efficiency, decision-making, and safety. Lack of innovation culture, training, and regulation harms health and safety. Using AI promises to overcome these hurdles by minimising risks, improving worker well-being, and safe work environment. The Ghanaian industry study focus and small sample size may prejudice, as the limitations of the study. Samples must be larger and more diversified to generalise. The practical implication is that Ghanaian builders may use the study’s findings. Understanding AI’s potential and limitations helps them develop AI solutions and problem-solving methodologies. Safety, cost, and worker well-being can improve. The successful integration of AI in construction health and safety can affect society. AI can reduce workplace accidents and improve productivity, well-being, and healthcare costs. This work adds to the growing body of knowledge on AI’s building safety applications in emerging economies like Ghana. It identifies environmental restrictions and enables governments, industry leaders, and researchers to develop and implement AI solutions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Building accidents and fatalities are prevalent, especially in rising nations like Ghana, despite rapid technical developments. Weak regulations, training, and change resistance typically undermine traditional safety measures. This study aimed to identify potential obstacles that prevent the implementation of artificial intelligence (AI) in construction health and safety in Ghana. A survey research approach was employed to get the study population, which consisted of 110 construction experts made up of project managers, site engineers, skilled workers, and safety officers complete the questionnaire. Data analysis included descriptive statistics, chi-square, and regression. According to varied demographic responses, AI increases design and engineering, safety and security, and human resources efficiency, decision-making, and safety. Lack of innovation culture, training, and regulation harms health and safety. Using AI promises to overcome these hurdles by minimising risks, improving worker well-being, and safe work environment. The Ghanaian industry study focus and small sample size may prejudice, as the limitations of the study. Samples must be larger and more diversified to generalise. The practical implication is that Ghanaian builders may use the study’s findings. Understanding AI’s potential and limitations helps them develop AI solutions and problem-solving methodologies. Safety, cost, and worker well-being can improve. The successful integration of AI in construction health and safety can affect society. AI can reduce workplace accidents and improve productivity, well-being, and healthcare costs. This work adds to the growing body of knowledge on AI’s building safety applications in emerging economies like Ghana. It identifies environmental restrictions and enables governments, industry leaders, and researchers to develop and implement AI solutions.
4.
Gielens, Katrijn
Making AI work in retail: The vital role of human interaction Journal Article
In: Journal of Retailing, vol. 100, no. 2, pp. 161–165, 2024, ISSN: 0022-4359.
@article{gielens_making_2024,
title = {Making AI work in retail: The vital role of human interaction},
author = {Katrijn Gielens},
url = {https://www.sciencedirect.com/science/article/pii/S0022435924000241},
doi = {10.1016/j.jretai.2024.05.006},
issn = {0022-4359},
year = {2024},
date = {2024-06-01},
urldate = {2024-11-24},
journal = {Journal of Retailing},
volume = {100},
number = {2},
pages = {161–165},
abstract = {In 2023, artificial intelligence (AI) and in-store technology were anticipated to be transformative forces in retail. Industry leaders and tech enthusiasts forecasted that these innovations would revolutionize customer experiences and operational efficiencies. AI and in-store technology undoubtedly offer numerous opportunities to enhance retail operations, including streamlining customer assistance, improving inventory management, and personalizing shopping experiences. Despite these advancements, however, consumer satisfaction with in-store technology continues to be a significant challenge.
Retailers are increasingly investing in AI to enhance their operations and customer experiences. A recent study reveals that grocers plan to boost their AI spending by 400 % by 2025, with 73 % of grocery tech executives expecting AI to be integrated into most or all of their software capabilities by then. This significant investment underscores the industryʼs confidence in AI's potential to transform retail operations. Consumers also show strong interest in AI enhancements. A survey of 20,000 consumers across 26 countries found that over half are eager for AI features like virtual assistants (55 %) and AI applications (59 %) while shopping. Personalization and targeted offerings are particularly appealing, with 52 % of consumers interested in receiving information, promotions, and advertisements tailored to their specific interests. These AI-driven advancements promise to significantly enhance the shopping experience by making it more convenient and personalized.
However, as we progress further into 2024, disappointment is setting in. The disparity between AIʼs potential and its practical applications has become increasingly apparent, raising questions about the need for a more human touch in retail. Despite the potential benefits, many consumers remain dissatisfied with their online and in-store technology experiences. The same survey revealed that only about one-third of users are satisfied with virtual assistants, while nearly 20 % are so disappointed that they do not want to use them again. This underscores a significant gap between consumer expectations and the current capabilities of AI technologies. Similarly, a survey of 11,000 global consumers found that retailers are struggling to meet shoppers' expectations for in-store tech.5 Security concerns and a lack of staff support exacerbate consumer dissatisfaction. When provided with several in-store device options—such as self-serve checkouts, in-store tablets, and point-of-sale (POS) devices—only handheld store scanners were considered by the majority of respondents (51 %) to improve the shopping experience. Common complaints include issues with self-serve checkouts (51 %) and a lack of guidance on using various in-store technologies (46 %). These frustrations negatively impact consumer enthusiasm for next-generation AI-powered retail technology. Only 20 % of consumers are excited about AIʼs potential to improve and personalize the retail experience, and just 19 % expect to use no-touch shopping. This indicates a broader hesitation toward embracing advanced in-store technologies due to past experiences.
While AI and in-store technology undeniably offer numerous opportunities for enhancing retail operations and customer experiences, to fully realize these benefits, retailers must address the current gaps in consumer satisfaction. Further research is thus essential to guide retailers in making informed decisions about integrating AI and in-store technology. Moreover, this research should focus on identifying the optimal balance between technological advancements and human interaction to enhance the overall customer experience. By understanding consumer preferences, security concerns, and the effectiveness of various technologies, retailers can implement solutions that meet customer expectations and improve satisfaction. Additionally, comprehensive studies can help retailers anticipate potential challenges and develop strategies to seamlessly blend AI with personalized human service, ensuring a more effective and satisfying shopping experience for all.
In the remainder, I will explore the opportunities that AI presents for both online and offline shopping, while also addressing the current disappointments and challenges associated with its applications in more detail. In doing so, I hope to create awareness for the need for a more comprehensive understanding and provide food for thought of how AI can be effectively integrated into the retail landscape to meet consumer expectations and enhance the overall shopping experience.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In 2023, artificial intelligence (AI) and in-store technology were anticipated to be transformative forces in retail. Industry leaders and tech enthusiasts forecasted that these innovations would revolutionize customer experiences and operational efficiencies. AI and in-store technology undoubtedly offer numerous opportunities to enhance retail operations, including streamlining customer assistance, improving inventory management, and personalizing shopping experiences. Despite these advancements, however, consumer satisfaction with in-store technology continues to be a significant challenge.
Retailers are increasingly investing in AI to enhance their operations and customer experiences. A recent study reveals that grocers plan to boost their AI spending by 400 % by 2025, with 73 % of grocery tech executives expecting AI to be integrated into most or all of their software capabilities by then. This significant investment underscores the industryʼs confidence in AI's potential to transform retail operations. Consumers also show strong interest in AI enhancements. A survey of 20,000 consumers across 26 countries found that over half are eager for AI features like virtual assistants (55 %) and AI applications (59 %) while shopping. Personalization and targeted offerings are particularly appealing, with 52 % of consumers interested in receiving information, promotions, and advertisements tailored to their specific interests. These AI-driven advancements promise to significantly enhance the shopping experience by making it more convenient and personalized.
However, as we progress further into 2024, disappointment is setting in. The disparity between AIʼs potential and its practical applications has become increasingly apparent, raising questions about the need for a more human touch in retail. Despite the potential benefits, many consumers remain dissatisfied with their online and in-store technology experiences. The same survey revealed that only about one-third of users are satisfied with virtual assistants, while nearly 20 % are so disappointed that they do not want to use them again. This underscores a significant gap between consumer expectations and the current capabilities of AI technologies. Similarly, a survey of 11,000 global consumers found that retailers are struggling to meet shoppers' expectations for in-store tech.5 Security concerns and a lack of staff support exacerbate consumer dissatisfaction. When provided with several in-store device options—such as self-serve checkouts, in-store tablets, and point-of-sale (POS) devices—only handheld store scanners were considered by the majority of respondents (51 %) to improve the shopping experience. Common complaints include issues with self-serve checkouts (51 %) and a lack of guidance on using various in-store technologies (46 %). These frustrations negatively impact consumer enthusiasm for next-generation AI-powered retail technology. Only 20 % of consumers are excited about AIʼs potential to improve and personalize the retail experience, and just 19 % expect to use no-touch shopping. This indicates a broader hesitation toward embracing advanced in-store technologies due to past experiences.
While AI and in-store technology undeniably offer numerous opportunities for enhancing retail operations and customer experiences, to fully realize these benefits, retailers must address the current gaps in consumer satisfaction. Further research is thus essential to guide retailers in making informed decisions about integrating AI and in-store technology. Moreover, this research should focus on identifying the optimal balance between technological advancements and human interaction to enhance the overall customer experience. By understanding consumer preferences, security concerns, and the effectiveness of various technologies, retailers can implement solutions that meet customer expectations and improve satisfaction. Additionally, comprehensive studies can help retailers anticipate potential challenges and develop strategies to seamlessly blend AI with personalized human service, ensuring a more effective and satisfying shopping experience for all.
In the remainder, I will explore the opportunities that AI presents for both online and offline shopping, while also addressing the current disappointments and challenges associated with its applications in more detail. In doing so, I hope to create awareness for the need for a more comprehensive understanding and provide food for thought of how AI can be effectively integrated into the retail landscape to meet consumer expectations and enhance the overall shopping experience.
Retailers are increasingly investing in AI to enhance their operations and customer experiences. A recent study reveals that grocers plan to boost their AI spending by 400 % by 2025, with 73 % of grocery tech executives expecting AI to be integrated into most or all of their software capabilities by then. This significant investment underscores the industryʼs confidence in AI's potential to transform retail operations. Consumers also show strong interest in AI enhancements. A survey of 20,000 consumers across 26 countries found that over half are eager for AI features like virtual assistants (55 %) and AI applications (59 %) while shopping. Personalization and targeted offerings are particularly appealing, with 52 % of consumers interested in receiving information, promotions, and advertisements tailored to their specific interests. These AI-driven advancements promise to significantly enhance the shopping experience by making it more convenient and personalized.
However, as we progress further into 2024, disappointment is setting in. The disparity between AIʼs potential and its practical applications has become increasingly apparent, raising questions about the need for a more human touch in retail. Despite the potential benefits, many consumers remain dissatisfied with their online and in-store technology experiences. The same survey revealed that only about one-third of users are satisfied with virtual assistants, while nearly 20 % are so disappointed that they do not want to use them again. This underscores a significant gap between consumer expectations and the current capabilities of AI technologies. Similarly, a survey of 11,000 global consumers found that retailers are struggling to meet shoppers' expectations for in-store tech.5 Security concerns and a lack of staff support exacerbate consumer dissatisfaction. When provided with several in-store device options—such as self-serve checkouts, in-store tablets, and point-of-sale (POS) devices—only handheld store scanners were considered by the majority of respondents (51 %) to improve the shopping experience. Common complaints include issues with self-serve checkouts (51 %) and a lack of guidance on using various in-store technologies (46 %). These frustrations negatively impact consumer enthusiasm for next-generation AI-powered retail technology. Only 20 % of consumers are excited about AIʼs potential to improve and personalize the retail experience, and just 19 % expect to use no-touch shopping. This indicates a broader hesitation toward embracing advanced in-store technologies due to past experiences.
While AI and in-store technology undeniably offer numerous opportunities for enhancing retail operations and customer experiences, to fully realize these benefits, retailers must address the current gaps in consumer satisfaction. Further research is thus essential to guide retailers in making informed decisions about integrating AI and in-store technology. Moreover, this research should focus on identifying the optimal balance between technological advancements and human interaction to enhance the overall customer experience. By understanding consumer preferences, security concerns, and the effectiveness of various technologies, retailers can implement solutions that meet customer expectations and improve satisfaction. Additionally, comprehensive studies can help retailers anticipate potential challenges and develop strategies to seamlessly blend AI with personalized human service, ensuring a more effective and satisfying shopping experience for all.
In the remainder, I will explore the opportunities that AI presents for both online and offline shopping, while also addressing the current disappointments and challenges associated with its applications in more detail. In doing so, I hope to create awareness for the need for a more comprehensive understanding and provide food for thought of how AI can be effectively integrated into the retail landscape to meet consumer expectations and enhance the overall shopping experience.
5.
Meisinger, Norman
Blue collar with tie: a human-centered reformulation of the ironies of automation Journal Article
In: AI & SOCIETY, vol. 38, no. 6, pp. 2653–2657, 2023, ISSN: 1435-5655.
@article{meisinger_blue_2023,
title = {Blue collar with tie: a human-centered reformulation of the ironies of automation},
author = {Norman Meisinger},
url = {https://doi.org/10.1007/s00146-021-01320-y},
doi = {10.1007/s00146-021-01320-y},
issn = {1435-5655},
year = {2023},
date = {2023-12-01},
urldate = {2024-11-24},
journal = {AI & SOCIETY},
volume = {38},
number = {6},
pages = {2653–2657},
abstract = {When Lisanne Bainbridge wrote about counterintuitive consequences of the increasing human–machine interaction, she concentrated on the resulting issues for system performance, stability, and safety. Now, decades later, however, the automized work environment is substantially more pervasive, sophisticated, and interactive. Current advances in machine learning technologies reshape the value, meaning, and future of the human workforce. While the ‘human factor’ still challenges automation system architects, inconspicuously new ironic settings have evolved that only become distinctly evident from a human-centered perspective. This brief essay discusses the role of the human workforce in human–machine interaction as machine learning continues to improve, and it points to the counterintuitive insight that although the demand for blue-collar workers may decrease, exactly this labor class increasingly enters more privileged working domains and establishes itself henceforth as ‘blue collar with tie.’},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
When Lisanne Bainbridge wrote about counterintuitive consequences of the increasing human–machine interaction, she concentrated on the resulting issues for system performance, stability, and safety. Now, decades later, however, the automized work environment is substantially more pervasive, sophisticated, and interactive. Current advances in machine learning technologies reshape the value, meaning, and future of the human workforce. While the ‘human factor’ still challenges automation system architects, inconspicuously new ironic settings have evolved that only become distinctly evident from a human-centered perspective. This brief essay discusses the role of the human workforce in human–machine interaction as machine learning continues to improve, and it points to the counterintuitive insight that although the demand for blue-collar workers may decrease, exactly this labor class increasingly enters more privileged working domains and establishes itself henceforth as ‘blue collar with tie.’
6.
Süße, Thomas; Kobert, Maria; Kries, Caroline
Human-AI interaction in remanufacturing: exploring shop floor workers’ behavioural patterns within a specific human-AI system Journal Article
In: Labour and Industry, vol. 33, no. 3, pp. 344–363, 2023, ISSN: 1030-1763, (Publisher: Routledge _eprint: https://doi.org/10.1080/10301763.2023.2251103).
@article{suse_human-ai_2023,
title = {Human-AI interaction in remanufacturing: exploring shop floor workers’ behavioural patterns within a specific human-AI system},
author = {Thomas Süße and Maria Kobert and Caroline Kries},
url = {https://doi.org/10.1080/10301763.2023.2251103},
doi = {10.1080/10301763.2023.2251103},
issn = {1030-1763},
year = {2023},
date = {2023-07-01},
urldate = {2024-11-24},
journal = {Labour and Industry},
volume = {33},
number = {3},
pages = {344–363},
abstract = {Artificial intelligence (AI) is increasingly discussed as an innovation enabler for the enhancement of circular economy (CE) approaches in industries. The further deployment of intelligent technologies is considered to be very promising particularly in remanufacturing, which can be regarded as an implementation approach of CE at a firm level. AI’s potential to contribute to advancements in remanufacturing can be traced back to these modern technologies’ extended capacities of supporting and assisting humans during rather manual processes which are regarded as more common in remanufacturing than in traditional linear production. As a result, we argue that in future application scenarios, humans are going to interact more often with AI agents who may direct and assist humans’ behaviour and decision-making processes. We assume that a better understanding of the specific dynamics and novel aspects of these kind of newly emerging human-AI systems is a key prerequisite for sustainable process innovation, particularly in remanufacturing organisations. However, empirical-based contributions about humans’ behavioural changes in interaction with AI agents have so far been rather rare and limited, especially in the field of remanufacturing and CE. In this article, we seek to contribute to this gap in research by exploring the interaction between shop floor workers and an AI agent based on a case study research approach at a plant of a German automotive supplier that is remanufacturing used parts. We conducted semi-structured interviews among the shop floor workers who are involved in a joint decision-making task with an AI agent. We interpret the findings of our qualitative data in the light of related research in the field of AI in CE, AI implementation in organisation and human-AI interaction literature. In summary, our analysis reveals 13 behavioural patterns that shop floor workers reported on referring to their interaction with the AI agent. The behavioural patterns are systemised into a cognitive, emotional and social dimension of a competence framework. These findings shall contribute to a more specific understanding about how humans interact with AI agents at work, while considering the specific context variables of the interaction paradigm and the AI agent’s role during joint decision-making in a human-AI system. Implications for literature in the field of human-AI interaction as well as AI implementation in organisations with a particular focus on CE are discussed.},
note = {Publisher: Routledge
_eprint: https://doi.org/10.1080/10301763.2023.2251103},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Artificial intelligence (AI) is increasingly discussed as an innovation enabler for the enhancement of circular economy (CE) approaches in industries. The further deployment of intelligent technologies is considered to be very promising particularly in remanufacturing, which can be regarded as an implementation approach of CE at a firm level. AI’s potential to contribute to advancements in remanufacturing can be traced back to these modern technologies’ extended capacities of supporting and assisting humans during rather manual processes which are regarded as more common in remanufacturing than in traditional linear production. As a result, we argue that in future application scenarios, humans are going to interact more often with AI agents who may direct and assist humans’ behaviour and decision-making processes. We assume that a better understanding of the specific dynamics and novel aspects of these kind of newly emerging human-AI systems is a key prerequisite for sustainable process innovation, particularly in remanufacturing organisations. However, empirical-based contributions about humans’ behavioural changes in interaction with AI agents have so far been rather rare and limited, especially in the field of remanufacturing and CE. In this article, we seek to contribute to this gap in research by exploring the interaction between shop floor workers and an AI agent based on a case study research approach at a plant of a German automotive supplier that is remanufacturing used parts. We conducted semi-structured interviews among the shop floor workers who are involved in a joint decision-making task with an AI agent. We interpret the findings of our qualitative data in the light of related research in the field of AI in CE, AI implementation in organisation and human-AI interaction literature. In summary, our analysis reveals 13 behavioural patterns that shop floor workers reported on referring to their interaction with the AI agent. The behavioural patterns are systemised into a cognitive, emotional and social dimension of a competence framework. These findings shall contribute to a more specific understanding about how humans interact with AI agents at work, while considering the specific context variables of the interaction paradigm and the AI agent’s role during joint decision-making in a human-AI system. Implications for literature in the field of human-AI interaction as well as AI implementation in organisations with a particular focus on CE are discussed.
7.
Chu, Mengdi; Zong, Keyu; Shu, Xin; Gong, Jiangtao; Lu, Zicong; Guo, Kaimin; Dai, Xinyi; Zhou, Guyue
Work with AI and Work for AI: Autonomous Vehicle Safety Drivers' Lived Experiences Miscellaneous
2023, (arXiv:2303.04986).
@misc{chu_work_2023,
title = {Work with AI and Work for AI: Autonomous Vehicle Safety Drivers' Lived Experiences},
author = {Mengdi Chu and Keyu Zong and Xin Shu and Jiangtao Gong and Zicong Lu and Kaimin Guo and Xinyi Dai and Guyue Zhou},
url = {http://arxiv.org/abs/2303.04986},
doi = {10.48550/arXiv.2303.04986},
year = {2023},
date = {2023-03-01},
urldate = {2024-11-24},
publisher = {arXiv},
abstract = {The development of Autonomous Vehicle (AV) has created a novel job, the safety driver, recruited from experienced drivers to supervise and operate AV in numerous driving missions. Safety drivers usually work with non-perfect AV in high-risk real-world traffic environments for road testing tasks. However, this group of workers is under-explored in the HCI community. To fill this gap, we conducted semi-structured interviews with 26 safety drivers. Our results present how safety drivers cope with defective algorithms and shape and calibrate their perceptions while working with AV. We found that, as front-line workers, safety drivers are forced to take risks accumulated from the AV industry upstream and are also confronting restricted self-development in working for AV development. We contribute the first empirical evidence of the lived experience of safety drivers, the first passengers in the development of AV, and also the grassroots workers for AV, which can shed light on future human-AI interaction research.},
note = {arXiv:2303.04986},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
The development of Autonomous Vehicle (AV) has created a novel job, the safety driver, recruited from experienced drivers to supervise and operate AV in numerous driving missions. Safety drivers usually work with non-perfect AV in high-risk real-world traffic environments for road testing tasks. However, this group of workers is under-explored in the HCI community. To fill this gap, we conducted semi-structured interviews with 26 safety drivers. Our results present how safety drivers cope with defective algorithms and shape and calibrate their perceptions while working with AV. We found that, as front-line workers, safety drivers are forced to take risks accumulated from the AV industry upstream and are also confronting restricted self-development in working for AV development. We contribute the first empirical evidence of the lived experience of safety drivers, the first passengers in the development of AV, and also the grassroots workers for AV, which can shed light on future human-AI interaction research.
8.
Walter, Christoph; Bexten, Simone; Felsch, Torsten; Shysh, Myroslav; Elkmann, Norbert
Safety considerations for autonomous, modular robotics in aerospace manufacturing Journal Article
In: Frontiers in Robotics and AI, vol. 9, 2022, ISSN: 2296-9144, (Publisher: Frontiers).
@article{walter_safety_2022,
title = {Safety considerations for autonomous, modular robotics in aerospace manufacturing},
author = {Christoph Walter and Simone Bexten and Torsten Felsch and Myroslav Shysh and Norbert Elkmann},
url = {https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2022.1024594/full},
doi = {10.3389/frobt.2022.1024594},
issn = {2296-9144},
year = {2022},
date = {2022-11-01},
urldate = {2024-11-24},
journal = {Frontiers in Robotics and AI},
volume = {9},
abstract = {Industrial robots are versatile machines that can be used to implement numerous tasks. They have been successful in applications where–after integration and commissioning–a more or less static and repetitive behaviour in conjunction with closed work cells is sufficient. In aerospace manufacturing, robots still struggle to compete against either specialized machines or manual labour. This can be attributed to complex or custom parts and/or small batch sizes. Here, applicability of robots can be improved by enabling collaborative use-cases. When fixed protective fences are not desired due to handling problems of the large parts involved, sensor-based approaches like speed and separation monitoring (SSM) are required. This contribution is about how to construct dynamic volumes of space around a robot as well as around a person in the way that their combination satisfies required separation distance between robot and person. The goal was to minimize said distance by calculating volumes both adaptively and as precisely as possible given the available information. We used a voxel-based method to compute the robot safety space that includes worst-case breaking behaviour. We focused on providing a worst-case representation considering all possible breaking variations. Our approach to generate the person safety space is based on an outlook for 2D camera, AI-based workspace surveillance.},
note = {Publisher: Frontiers},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Industrial robots are versatile machines that can be used to implement numerous tasks. They have been successful in applications where–after integration and commissioning–a more or less static and repetitive behaviour in conjunction with closed work cells is sufficient. In aerospace manufacturing, robots still struggle to compete against either specialized machines or manual labour. This can be attributed to complex or custom parts and/or small batch sizes. Here, applicability of robots can be improved by enabling collaborative use-cases. When fixed protective fences are not desired due to handling problems of the large parts involved, sensor-based approaches like speed and separation monitoring (SSM) are required. This contribution is about how to construct dynamic volumes of space around a robot as well as around a person in the way that their combination satisfies required separation distance between robot and person. The goal was to minimize said distance by calculating volumes both adaptively and as precisely as possible given the available information. We used a voxel-based method to compute the robot safety space that includes worst-case breaking behaviour. We focused on providing a worst-case representation considering all possible breaking variations. Our approach to generate the person safety space is based on an outlook for 2D camera, AI-based workspace surveillance.
9.
Tyson, Laura D.; Zysman, John
Automation, AI & Work Journal Article
In: Daedalus, vol. 151, no. 2, pp. 256–271, 2022, ISSN: 0011-5266.
@article{tyson_automation_2022,
title = {Automation, AI & Work},
author = {Laura D. Tyson and John Zysman},
url = {https://doi.org/10.1162/daed_a_01914},
doi = {10.1162/daed_a_01914},
issn = {0011-5266},
year = {2022},
date = {2022-05-01},
urldate = {2024-11-24},
journal = {Daedalus},
volume = {151},
number = {2},
pages = {256–271},
abstract = {We characterize artificial intelligence as “routine-biased technological change on
steroids,” adding intelligence to automation tools that substitute for humans in physical
tasks and substituting for humans in routine and increasingly nonroutine cognitive tasks.
We predict how AI will displace humans from existing tasks while increasing demand for
humans in new tasks in both manufacturing and services. We also examine the effects of
AI-enabled digital platforms on labor. Our conjecture is that AI will continue, even
intensify, automation's adverse effects on labor, including the polarization of
employment, stagnant wage growth for middle- and low-skill workers, growing inequality,
and a lack of good jobs. Though there likely will be enough jobs to keep pace with the
slow growth of the labor supply in the advanced economies, we are skeptical that AI and
ongoing automation will support the creation of enough good jobs. We doubt that the
anticipated productivity and growth benefits of AI will be widely shared, predicting
instead that they will fuel more inequality. Yet we are optimistic that interventions can
mitigate or offset AI's adverse effects on labor. Ultimately, how the benefits of
intelligent automation tools are realized and shared depends not simply on their
technological design but on the design of intelligent policies.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We characterize artificial intelligence as “routine-biased technological change on
steroids,” adding intelligence to automation tools that substitute for humans in physical
tasks and substituting for humans in routine and increasingly nonroutine cognitive tasks.
We predict how AI will displace humans from existing tasks while increasing demand for
humans in new tasks in both manufacturing and services. We also examine the effects of
AI-enabled digital platforms on labor. Our conjecture is that AI will continue, even
intensify, automation's adverse effects on labor, including the polarization of
employment, stagnant wage growth for middle- and low-skill workers, growing inequality,
and a lack of good jobs. Though there likely will be enough jobs to keep pace with the
slow growth of the labor supply in the advanced economies, we are skeptical that AI and
ongoing automation will support the creation of enough good jobs. We doubt that the
anticipated productivity and growth benefits of AI will be widely shared, predicting
instead that they will fuel more inequality. Yet we are optimistic that interventions can
mitigate or offset AI's adverse effects on labor. Ultimately, how the benefits of
intelligent automation tools are realized and shared depends not simply on their
technological design but on the design of intelligent policies.
steroids,” adding intelligence to automation tools that substitute for humans in physical
tasks and substituting for humans in routine and increasingly nonroutine cognitive tasks.
We predict how AI will displace humans from existing tasks while increasing demand for
humans in new tasks in both manufacturing and services. We also examine the effects of
AI-enabled digital platforms on labor. Our conjecture is that AI will continue, even
intensify, automation's adverse effects on labor, including the polarization of
employment, stagnant wage growth for middle- and low-skill workers, growing inequality,
and a lack of good jobs. Though there likely will be enough jobs to keep pace with the
slow growth of the labor supply in the advanced economies, we are skeptical that AI and
ongoing automation will support the creation of enough good jobs. We doubt that the
anticipated productivity and growth benefits of AI will be widely shared, predicting
instead that they will fuel more inequality. Yet we are optimistic that interventions can
mitigate or offset AI's adverse effects on labor. Ultimately, how the benefits of
intelligent automation tools are realized and shared depends not simply on their
technological design but on the design of intelligent policies.
10.
Szajna, Andrzej; Kostrzewski, Mariusz
AR-AI Tools as a Response to High Employee Turnover and Shortages in Manufacturing during Regular, Pandemic, and War Times Journal Article
In: Sustainability, vol. 14, no. 11, pp. 6729, 2022, ISSN: 2071-1050, (Number: 11 Publisher: Multidisciplinary Digital Publishing Institute).
@article{szajna_ar-ai_2022,
title = {AR-AI Tools as a Response to High Employee Turnover and Shortages in Manufacturing during Regular, Pandemic, and War Times},
author = {Andrzej Szajna and Mariusz Kostrzewski},
url = {https://www.mdpi.com/2071-1050/14/11/6729},
doi = {10.3390/su14116729},
issn = {2071-1050},
year = {2022},
date = {2022-01-01},
urldate = {2024-11-24},
journal = {Sustainability},
volume = {14},
number = {11},
pages = {6729},
abstract = {The world faces the continuously increasing issue of a lack of skilled employees, staff migration, and turnover. It is strengthened by unexpected situations such as wars, pandemics, and other civilization crises. Solutions are sought and researched in various branches of industry and academia, including engineering, social sciences, management, and political and computer sciences. From the viewpoint of this paper, this is a side topic of Industry 4.0 and, more specifically, sustainability in working environments, and the issue is related to production employees who perform manual operations. Some of the tasks cannot be carried out under robotization or automation; therefore, novel human-work support tools are expected. This paper presents such highly demanded support tools related to augmented reality (AR) and artificial intelligence (AI). First, a panoramic literature review is given. Secondly, the authors explain the main objective of the presented contribution. Then the authors’ achievements are described—the R&D focus on such solutions and the introduction of the developed tools that are based on AR and AI. Benefits connected to the AR-AI technology applications are presented in terms of both time savings with the tool usage and job simplification, enabling inexperienced, unskilled, or less skilled employees to perform the work in the selected manual production processes.},
note = {Number: 11
Publisher: Multidisciplinary Digital Publishing Institute},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The world faces the continuously increasing issue of a lack of skilled employees, staff migration, and turnover. It is strengthened by unexpected situations such as wars, pandemics, and other civilization crises. Solutions are sought and researched in various branches of industry and academia, including engineering, social sciences, management, and political and computer sciences. From the viewpoint of this paper, this is a side topic of Industry 4.0 and, more specifically, sustainability in working environments, and the issue is related to production employees who perform manual operations. Some of the tasks cannot be carried out under robotization or automation; therefore, novel human-work support tools are expected. This paper presents such highly demanded support tools related to augmented reality (AR) and artificial intelligence (AI). First, a panoramic literature review is given. Secondly, the authors explain the main objective of the presented contribution. Then the authors’ achievements are described—the R&D focus on such solutions and the introduction of the developed tools that are based on AR and AI. Benefits connected to the AR-AI technology applications are presented in terms of both time savings with the tool usage and job simplification, enabling inexperienced, unskilled, or less skilled employees to perform the work in the selected manual production processes.