Library

@article{liu_how_2024,

title = {How AI powers ESG performance in China's digital frontier?},

author = {Xukang Liu and Chaoqun Ma and Yi-Shuai Ren},

url = {https://www.sciencedirect.com/science/article/pii/S1544612324013539},

doi = {10.1016/j.frl.2024.106324},

issn = {1544-6123},

year  = {2024},

date = {2024-12-01},

urldate = {2024-11-22},

journal = {Finance Research Letters},

volume = {70},

pages = {106324},

abstract = {This study investigates the effect of AI on corporate ESG performance using data from Chinese A-share listed firms from 2009–2022. The results indicate that AI enhances corporate R&D investment and the degree of digital transformation, therefore improving ESG performance. The driving effect of AI on ESG performance is more pronounced in firms located in provinces with greater digital financial inclusion. Our findings are robust. This study underscores the importance of AI development in enhancing corporate ESG performance, offering both theoretical support and practical recommendations for establishing a greener and sustainable economic system in China's digital frontier.},

keywords = {AI and ESG},

pubstate = {published},

tppubtype = {article}

}

@article{li_using_2024,

title = {Using Artificial Intelligence in ESG Assurance},

author = {Nichole Li and Meehyun Kim and Jun Dai and Miklos A. Vasarhelyi},

url = {https://doi.org/10.2308/JETA-2022-054},

doi = {10.2308/JETA-2022-054},

issn = {1554-1908},

year  = {2024},

date = {2024-10-01},

urldate = {2024-11-22},

journal = {Journal of Emerging Technologies in Accounting},

volume = {21},

number = {2},

pages = {83–99},

abstract = {As environmental, social, and governance (ESG) reporting has become a mainstream channel for companies to communicate their commitment to sustainability issues, the need for reliable and transparent ESG reports is increasing. However, research on ESG assurance is still in its early stages. ESG assurance poses more challenges than traditional financial auditing due to the diverse subjects and types of information in ESG reports. This paper proposes using artificial intelligence (AI) technologies and exogenous data as solutions. It discusses how AI can enhance the efficiency and effectiveness of ESG assurance by assessing vast and extensive data. This paper also explores AI’s application throughout the general ESG assurance process and contributes to the discussion on providing high-quality ESG assurance services. Additionally, it provides practical implications for auditors, regulators, and stakeholders.},

keywords = {AI and ESG},

pubstate = {published},

tppubtype = {article}

}

@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 = {AI and Blue Collar Work},

pubstate = {published},

tppubtype = {article}

}

@article{ferraro_paradoxes_2024,

title = {The paradoxes of generative AI-enabled customer service: A guide for managers},

author = {Carla Ferraro and Vlad Demsar and Sean Sands and Mariluz Restrepo and Colin Campbell},

url = {https://www.sciencedirect.com/science/article/pii/S0007681324000582},

doi = {10.1016/j.bushor.2024.04.013},

issn = {0007-6813},

year  = {2024},

date = {2024-09-01},

urldate = {2024-11-24},

journal = {Business Horizons},

volume = {67},

number = {5},

pages = {549–559},

series = {SPECIAL ISSUE: WRITTEN BY CHATGPT},

abstract = {Generative artificial intelligence (GenAI) presents a disruptive innovation for brands and society, and the power of which is still yet to be realized. In the context of customer service, gen AI affords companies new possibilities to communicate, connect, and engage customers. This article draws on scholarly research and consultation with customer service leaders to present and discuss the possibilities for GenAI in the context of customer service, specifically GenAI chatbots. Importantly, this article presents potential paradoxes of GenAI-enabled customer service, adding to the debate about the role and impact of GenAI for brands. Specifically, we present six paradoxes of GenAI customer service: (1) connected yet isolated, (2) lower cost yet higher price, (3) higher quality yet less empathy, (4) satisfied yet frustrated, (5) personalized yet intrusive, and (6) powerful yet vulnerable. For each paradox, we suggest brand response strategies to mitigate downside and manage potential upside.},

keywords = {AI and Managerial Work},

pubstate = {published},

tppubtype = {article}

}

@article{berthon_trajectories_2024,

title = {Trajectories of AI technologies: Insights for managers},

author = {Pierre Berthon and Taylan Yalcin and Ekin Pehlivan and Tamara Rabinovich},

url = {https://www.sciencedirect.com/science/article/pii/S0007681324000284},

doi = {10.1016/j.bushor.2024.03.002},

issn = {0007-6813},

year  = {2024},

date = {2024-09-01},

urldate = {2024-11-24},

journal = {Business Horizons},

volume = {67},

number = {5},

pages = {461–470},

series = {SPECIAL ISSUE: WRITTEN BY CHATGPT},

abstract = {Generative artificial intelligence (GenAI) has long been considered a technology for the future. With the release of the chatbot ChatGPT 4, many now feel the future has arrived. Long in gestation, this new technology promises many benefits to humankind, but worries persist that as AI technology scales and comes to rival or exceed human intelligence, the servant may become the master. Amid such hyperbole, the more nuanced trajectories of this technology have been neglected. In this article, we use the Trajectories of Technology (ToT) framework developed by Berthon and colleagues to explore the disparate paths that AI has taken and will take in the coming years, especially in the form of chatbots. This framework provides managers with a conceptual tool to strategically plan for the enormous promises and perils of AI in general and of chatbots specifically.},

keywords = {AI and Managerial Work},

pubstate = {published},

tppubtype = {article}

}

@article{skilters_towards_2024,

title = {Towards A Human-AI Hybrid Medicine: Future Medicine — A Hybrid System Where AI Complements Instead of Replaces Humans},

author = {Jurģis Šķilters and Juris Pokrotnieks and Aleksejs Derovs},

url = {https://sciendo.com/article/10.2478/prolas-2024-0032},

doi = {10.2478/prolas-2024-0032},

year  = {2024},

date = {2024-09-01},

urldate = {2024-11-22},

journal = {Proceedings of the Latvian Academy of Sciences. Section B. Natural, Exact, and Applied Sciences.},

volume = {78},

number = {4},

pages = {233–238},

abstract = {Our paper provides a critical overview of the advantages, disadvantages, uncertainties, and challenges regarding AI application in medicine. Without denying the importance of the AI in medical applications, we are arguing for a hybrid and complementary view of future medical systems where powerful AI resources are integrated in and with human decision making.},

keywords = {AI and Medicine},

pubstate = {published},

tppubtype = {article}

}

@article{chen_shock_2024,

title = {Shock or empowerment? Artificial intelligence technology and corporate ESG performance},

author = {Jia Chen and Ning Wang and Tongzhi Lin and Baoliu Liu and Jin Hu},

url = {https://www.sciencedirect.com/science/article/pii/S0313592624001930},

doi = {10.1016/j.eap.2024.08.004},

issn = {0313-5926},

year  = {2024},

date = {2024-09-01},

urldate = {2024-11-22},

journal = {Economic Analysis and Policy},

volume = {83},

pages = {1080–1096},

abstract = {Artificial intelligence (AI) plays a significant role in realizing sustainable economic development. This paper uses the textual content of annual reports of listed companies to count 73 words frequencies related to AI and construct AI indicators through precise vocabulary. It also examines how AI affects environment, social, and governance (ESG) performance at the firm level using unbalanced panel data of Chinese listed firms from 2007 to 2022. The results indicate that the development of artificial intelligence has significantly improved the ESG performance of Chinese listed companies, and the conclusion still holds after a series of robustness tests. As a moderating variable, macroeconomic policy uncertainty reinforces the positive impact of AI on ESG performance. In terms of the impact mechanism, AI enhances firms’ ESG performance by increasing firms’ total factor productivity and R&D expenditures. The results of heterogeneity analysis show that AI has a significant positive impact on the ESG performance of non-state-owned firms, firms with executives without overseas backgrounds, and technology and capital-intensive firms. Compared with the western region, AI in the eastern and central regions has a more significant improvement effect on ESG performance. Our study deepens the knowledge and understanding of the role played by AI in the green development process at the micro level. It provides valuable suggestions and reflections for promoting AI development at the micro-enterprise level.},

keywords = {AI and ESG},

pubstate = {published},

tppubtype = {article}

}

@article{ali_synergizing_2024,

title = {Synergizing AI and business: Maximizing innovation, creativity, decision precision, and operational efficiency in high-tech enterprises},

author = {Muhammad Ali and Tariq Iqbal Khan and Mohammad Nisar Khattak and İrge Şener},

url = {https://www.sciencedirect.com/science/article/pii/S219985312400146X},

doi = {10.1016/j.joitmc.2024.100352},

issn = {2199-8531},

year  = {2024},

date = {2024-09-01},

urldate = {2024-11-22},

journal = {Journal of Open Innovation: Technology, Market, and Complexity},

volume = {10},

number = {3},

pages = {100352},

abstract = {The study was conducted on 125 US based high-tech firms from software engineering, hardware production, biotechnology, and telecommunications. Senior-level executives, including CEOs, board members, and CTOs, provided insights through structured questionnaires. Key findings indicate that AI adoption significantly enhances organizational capabilities in terms of employees’ innovation, creativity, and experimentation. Moreover, AI adaptation positively impacts decision making thus yielding more accurate and timely valuable decisions. These findings contribute to both theoretical understanding and managerial practice by guiding strategic investments in AI technologies, fostering innovation, and advocating for ethical AI deployment practices. Future study should examine longitudinal impacts across industries and regions to optimize benefits and minimize risks in digital transformation efforts. It should also integrate qualitative methods for deeper insights and appropriate AI governance systems.},

keywords = {AI and Business},

pubstate = {published},

tppubtype = {article}

}

@article{sayyadi_how_2024,

title = {How to improve data quality to empower business decision-making process and business strategy agility in the AI age},

author = {Mostafa Sayyadi},

url = {https://doi.org/10.1177/02663821241264705},

doi = {10.1177/02663821241264705},

issn = {0266-3821},

year  = {2024},

date = {2024-09-01},

urldate = {2024-11-22},

journal = {Business Information Review},

volume = {41},

number = {3},

pages = {124–129},

abstract = {Machine learning (ML) and predictive analytics (PA) can provide invaluable assistance for forecasting trends and informing decision-making using data. Considering the business strategy agility as a quick response to market dynamics, this article presents solutions to enhance data quality and management, model interpretation ability, and availability using Explainable AI, with cloud computing and distributed systems that can address scaling problems. When companies utilize these technologies wisely, they can gain an edge, achieve sustainable growth, and make informed decisions. We show how ML and PA can enhance decision-making and business strategy. We also remind that there are antecedents to data quality management: Data culture and Leadership, preparing the company to benefit from the information business strategy agility.},

note = {Publisher: SAGE Publications Ltd},

keywords = {AI and Business},

pubstate = {published},

tppubtype = {article}

}

@article{rani_impacts_2024,

title = {Impacts and ethics of using Artificial Intelligence (AI) by the Indian Police},

author = {Meena Rani},

url = {https://www.emerald.com/insight/content/doi/10.1108/pap-06-2023-0081/full/html},

doi = {10.1108/PAP-06-2023-0081},

issn = {2517-679X},

year  = {2024},

date = {2024-08-01},

urldate = {2024-11-22},

journal = {Public Administration and Policy},

volume = {27},

number = {2},

pages = {182–192},

abstract = {The paper aims to examine the impacts and ethics of utilizing Artificial Intelligence (AI) in Indian policing. It explores both the positive and negative consequences of using AI, as well as the ethical considerations that have be taken into account.,This study is based on secondary sources of information, such as national and international reports, journal articles, and institutional websites that discuss the use of AI technology by the police in India.,AI has proven to be effective in policing, from preventing crime to identifying criminals, by detecting potential crimes in advance with fewer resources and in more areas. In India, the police use AI technology not only for facial recognition but also for crime mapping, analysis, and building blocks. However, factors such as caste, religion, language, and gender continue to cause conflict. India has shown a strong interest in using AI technology for policing, and wishes to accelerate its implementation in various policing contexts, including law and order. This paper calls for an assessment of the complexities and uncertainties brought about by new technologies in policing with ethical considerations.,This paper can provide valuable insights for policy-makers, academics, and practitioners engaged in discussions and debates concerning the ethical considerations associated with the adoption of AI tools in policing practices.},

note = {Publisher: Emerald Publishing Limited},

keywords = {AI and Police Work},

pubstate = {published},

tppubtype = {article}

}

@article{mcguire_establishing_2024,

title = {Establishing the importance of co-creation and self-efficacy in creative collaboration with artificial intelligence},

author = {Jack McGuire and David De Cremer and Tim Van de Cruys},

url = {https://www.nature.com/articles/s41598-024-69423-2},

doi = {10.1038/s41598-024-69423-2},

issn = {2045-2322},

year  = {2024},

date = {2024-08-01},

urldate = {2024-11-22},

journal = {Scientific Reports},

volume = {14},

number = {1},

pages = {18525},

abstract = {The emergence of generative AI technologies has led to an increasing number of people collaborating with AI to produce creative works. Across two experimental studies, in which we carefully designed and programmed state-of-the-art human–AI interfaces, we examine how the design of generative AI systems influences human creativity (poetry writing). First, we find that people were most creative when writing a poem on their own, compared to first receiving a poem generated by an AI system and using sophisticated tools to edit it (Study 1). Following this, we demonstrate that this creativity deficit dissipates when people co-create with—not edit—AI and establish creative self-efficacy as an important mechanism in this process (Study 2). Thus, our findings indicate that people must occupy the role of a co-creator, not an editor, to reap the benefits of generative AI in the production of creative works.},

note = {Publisher: Nature Publishing Group},

keywords = {AI and Creative Work},

pubstate = {published},

tppubtype = {article}

}

@article{black_moving_2024,

title = {Moving beyond ‘proof points’: Factors underpinning AI-enabled business model transformation},

author = {Stuart Black and Daniel Samson and Alon Ellis},

url = {https://www.sciencedirect.com/science/article/pii/S0268401224000446},

doi = {10.1016/j.ijinfomgt.2024.102796},

issn = {0268-4012},

year  = {2024},

date = {2024-08-01},

urldate = {2024-11-22},

journal = {International Journal of Information Management},

volume = {77},

pages = {102796},

abstract = {Business model renewal is a key consideration for organizations, and AI (artificial intelligence) has been identified as a significant potential enabler for that renewal. However, while there are examples of emerging organizations using AI as a key basis of competitive advantage as well as examples of established organizations trialing AI technologies, there are relatively few examples of established organizations fundamentally transforming their business models through the use of AI. Through case studies underpinned by interviews with named executives of ten organizations and complemented by an applicability check involving 14 executives, advisors and practice-oriented academics, this paper presents an empirically supported set of factors linked to successful AI-enabled business model transformation as well as a model of interactions between these factors. Using a horizontal contrasting approach to articulate the difference between empirical findings and a literature based model, this paper moves from the language of potentially passive top management support towards the concept of proactive leadership and introduces tech-sensitive innovation culture, AI-sensitive risk tolerance and strategic process discipline into the dynamic capability lexicon. The insights of this paper can be used by managers to assess their readiness to move beyond traditional ‘proof-points’ and successfully undertake and accelerate AI-enabled business model transformation.},

keywords = {AI and Business},

pubstate = {published},

tppubtype = {article}

}

@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 = {AI and Blue Collar Work},

pubstate = {published},

tppubtype = {article}

}

@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 = {AI and Blue Collar Work},

pubstate = {published},

tppubtype = {article}

}

@article{rizzo_managers_2024,

title = {Do managers trust AI? An exploratory research based on social comparison theory},

author = {Cristian Rizzo and Giacomo Bagna and David Tuček},

url = {https://www.emerald.com/insight/content/doi/10.1108/md-10-2023-1971/full/html},

doi = {10.1108/MD-10-2023-1971},

issn = {0025-1747},

year  = {2024},

date = {2024-07-01},

urldate = {2024-11-22},

journal = {Management Decision},

volume = {ahead-of-print},

number = {ahead-of-print},

abstract = {The purpose of this study is to investigate managers’ decision-making processes when evaluating suggestions provided by human collaborators or artificial intelligence (AI) systems. We employed the framework of Social Comparison Theory (SCT) in the business context to examine the influence of varying social comparison orientation levels on managers’ willingness to accept advice in their organization.,A survey was conducted on a sample of 192 US managers, in which we carried out an experiment manipulating the source type (human vs AI) and assessing the potential moderating role of social comparison orientation. Results were analyzed using a moderation model by Hayes (2013).,Despite the growing consideration gained by AI systems, results showed a discernible preference for human-generated advice over those originating from Artificial Intelligence (AI) sources. Moreover, the moderation analysis indicated how low levels of social comparison orientation may lead managers to be more willing to accept advice from AI.,This study contributes to the current understanding of the interplay between social comparison orientation and managerial decision-making. Based on the results of this preliminary study that used a scenario-based experiment, future research could try to expand these findings by examining managerial behavior in a natural context using field experiments, or multiple case studies.,This is among the first studies that examine AI adoption in the organizational context, showing how AI may be used by managers to evade comparison among peers or other experts, thereby illuminating the role of individual factors in affecting managers’ decision-making.},

note = {Publisher: Emerald Publishing Limited},

keywords = {AI and Managerial Work},

pubstate = {published},

tppubtype = {article}

}

@article{donatz-fest_values_2024,

title = {Values? Camera? Action! An ethnography of an AI camera system used by the Netherlands Police},

author = {I. C. Donatz-Fest},

url = {https://doi.org/10.1080/10439463.2024.2370939},

doi = {10.1080/10439463.2024.2370939},

issn = {1043-9463},

year  = {2024},

date = {2024-07-01},

urldate = {2024-11-22},

journal = {Policing and Society},

volume = {0},

number = {0},

pages = {1–18},

abstract = {Police departments around the world implement algorithmic systems to enhance various policing tasks. Ensuring such innovations take place responsibly – with public values upheld – is essential for public organisations. This paper analyses how public values are safeguarded in the case of MONOcam, an algorithmic camera system designed and used by the Netherlands police. The system employs artificial intelligence to detect whether car drivers are holding a mobile device. MONOcam can be considered a good example of value-sensitive design; many measures were taken to safeguard public values in this algorithmic system. In pursuit of responsible implementation of algorithms, most calls and literature focus on such value-sensitive design. Less attention is paid to what happens beyond design. Building on 120+ hours of ethnographic observations as well as informal conversations and three semi-structured interviews, this research shows that public values deemed safeguarded in design are re-negotiated as the system is implemented and used in practice. These findings led to direct impact, as MONOcam was improved in response. This paper thus highlights that algorithmic system design is often based on an ideal world, but it is in the complexities and fuzzy realities of everyday professional routines and sociomaterial reality that these systems are enacted, and public values are renegotiated in the use of algorithms. While value-sensitive design is important, this paper shows that it offers no guarantees for safeguarding public values in practice.},

note = {Publisher: Routledge 

_eprint: https://doi.org/10.1080/10439463.2024.2370939},

keywords = {AI and Police Work},

pubstate = {published},

tppubtype = {article}

}

@article{smith_clinicians_2024,

title = {Clinicians and AI use: where is the professional guidance?},

author = {Helen Smith and John Downer and Jonathan Ives},

url = {https://jme.bmj.com/content/50/7/437},

doi = {10.1136/jme-2022-108831},

issn = {0306-6800, 1473-4257},

year  = {2024},

date = {2024-07-01},

urldate = {2024-11-22},

journal = {Journal of Medical Ethics},

volume = {50},

number = {7},

pages = {437–441},

abstract = {With the introduction of artificial intelligence (AI) to healthcare, there is also a need for professional guidance to support its use. New (2022) reports from National Health Service AI Lab & Health Education England focus on healthcare workers’ understanding and confidence in AI clinical decision support systems (AI-CDDSs), and are concerned with developing trust in, and the trustworthiness of these systems. While they offer guidance to aid developers and purchasers of such systems, they offer little specific guidance for the clinical users who will be required to use them in patient care. 

This paper argues that clinical, professional and reputational safety will be risked if this deficit of professional guidance for clinical users of AI-CDDSs is not redressed. We argue it is not enough to develop training for clinical users without first establishing professional guidance regarding the rights and expectations of clinical users. 

We conclude with a call to action for clinical regulators: to unite to draft guidance for users of AI-CDDS that helps manage clinical, professional and reputational risks. We further suggest that this exercise offers an opportunity to address fundamental issues in the use of AI-CDDSs; regarding, for example, the fair burden of responsibility for outcomes.},

note = {Publisher: Institute of Medical Ethics 

Section: Clinical ethics},

keywords = {AI and Medicine},

pubstate = {published},

tppubtype = {article}

}

@article{hsu_exploring_2024,

title = {Exploring the Digital Undertow: How generative AI impacts social categorizations in creative work},

author = {Greta Hsu and Beth A. Bechky},

url = {https://doi.org/10.1177/26317877241275118},

doi = {10.1177/26317877241275118},

issn = {2631-7877},

year  = {2024},

date = {2024-07-01},

urldate = {2024-11-22},

journal = {Organization Theory},

volume = {5},

number = {3},

pages = {26317877241275118},

abstract = {This paper examines generative AI’s broader implications for the social construction of categories. Building on the Orlikowski and Scott’s concept of the ‘digital undertow’, we consider how generative AI’s influence will likely extend beyond immediate technological benefits and lead to deeper shifts in the societal structures and occupational identities constituting conventional categories. We extrapolate from emerging findings that suggest that, while generative AI improves efficiency and the average quality of a creative product, it also tends to reduce the advantages of expertise and induce a homogenization of what is creatively produced as outputs. We consider how such dynamics might play out in the specific case of the changing roles of screenwriters and studio executives within the television and film industries. With this focused thought experiment, our overall aim is to draw attention to the broader implications of change brought forth by this technological innovation.},

note = {Publisher: SAGE Publications Ltd},

keywords = {AI and Creative Work},

pubstate = {published},

tppubtype = {article}

}

@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 = {AI and Blue Collar Work},

pubstate = {published},

tppubtype = {article}

}