IT
Artificial Intelligence: Applications in Production Processes, Service Delivery and Access, Ethical Implications, and Necessary Skills
Guest editor: Marcella Milana
Context
Seventy years after the introduction of the term “Artificial Intelligence” (AI) (McCarthy et al. 1955), which referred to the ability of a machine to emulate human cognitive and decision-making processes, AI has significantly transformed production processes as well as the delivery of and access to services, permeating the daily lives of citizens. This has been further accelerated by recent advancements in natural language processing (NLP), which enable the creation of highly realistic images, videos, and texts, with the usage of these tools being popularised by ChatGPT (Generative Pre-trained Transformer).
Countless potential applications of AI, particularly generative technologies, have emerged across multiple public and private sectors, either as support tools or as substitutes for workers. These applications range from managing administrative processes and procedures and producing tangible goods, to conducting scientific research and teaching-learning processes. AI systems can facilitate, accelerate, and optimise the management of administrative and production processes while reconfiguring internal operations and the relationships with external customers and users in public and private organisations. However, this significantly affects the quality and quantity of available jobs (Butera and De Michelis 2024). Additionally, it raises issues concerning the protection of workers from discrimination risks associated with employers using AI systems, starting with recruitment processes (Peruzzi 2024). AI can also assist scientific research activities (Rice et al. 2024), but it may compromise originality, validity, and academic integrity (Rahimi and Abadi 2023).
Thus, the adoption of AI systems by public entities and private companies entails risks and ethical challenges. For example, in managing administrative processes and procedures, protecting citizens’ rights may conflict with the goal of process optimisation (Grenci 2024). Meanwhile, generative AI used to seek information – for instance, medical advice – may provide useful and appropriate responses. Still, it requires the ability to assess the reliability and accuracy of those responses (Clerici et al. 2024). There is also the overarching concern of the relationship between humans and AI technologies becoming more vicious than virtuous. While humans are ‘conditioning’ AI by creating and testing algorithms that allow machines to emulate cognitive and decision-making processes, they can, in turn, become ‘conditioned’ by it (Martire 2024). This happens, for instance, when generative AI models used in the production, creation, and dissemination of news amplify misinformation (Pollicino and Dunn 2024).
Recently, the European Union has adopted AI regulations (Regulation (EU) 2024/1689) to define requirements and obligations for developers and operators, alongside guidelines for ethically correct uses of AI. These regulations are part of a broader EU framework aimed at supporting AI development while ensuring safety and respect for the fundamental rights of citizens and businesses. Additionally, several European and non-European countries (e.g., Germany, the United States, and China) have implemented national strategies to integrate AI into education and training systems for young people and adults, preparing them for AI use (Milana et al. 2024).
Different approaches can underpin such strategies. At the international level, some argue that enabling people to move from the mere acquisition of knowledge to its creation through generative AI requires a form of literacy that encompasses knowledge and understanding of how AI works, its use and application, its evaluation for potential content creation, and its ethical implications (Ng et al. 2021b). Others criticise this approach to AI literacy, arguing that it is limited to the knowledge of technological aspects related to AI and attitudes directly connected to its use (Markauskaite et al. 2022). Meanwhile, those who have examined, for example, the ability of employees to use AI in digital workplaces have identified four types of skills related to technology, work, human-machine interaction, and the learning necessary for the proper (and ethical) use of AI (Cetindamar et al. 2022).
Themes of Contributions
Issue 2/2025 aims to host contributions reflecting on the relationship between the technological development of AI – particularly generative AI – and its application in production processes and service delivery. The issue also aims to explore the skills required for AI development and use, how these skills can be acquired, and the ethical considerations tied to both the development and application of AI, including its role in scientific research. Using AI in production processes and service delivery can provide advantages to public administrations and businesses but also pose threats and challenges for workers, employees, and users. Moreover, it demands individuals who are proficient in the technological development of AI and, most importantly, skilled in its application and capable of critically evaluating its ethical implications in both contexts. It therefore becomes necessary to consider how the development and use of AI intersects with the development of educational pathways, also in relation to the adaptation needs of the working and ageing population.
This call invites multidisciplinary and interdisciplinary contributions addressing these issues, with a focus on one of the following themes:
- AI and public administrations
- AI and production processes
- AI and service delivery and access
- AI and workers’ rights
- AI and scientific research
- AI and ethics
- AI and security
- AI and rights
- AI and the acquisition of skills for competent development and use
Within these themes, we encourage the submission of original papers (not submitted to other journals), of length of between 5,000 and 8,000 words (Bibliographical standards of Sinappsi) for publication in Issue 2/2025. Submissions should be sent to the editorial office of Sinappsi ([email protected]) by 15 March 2025 to be submitted for refereeing (double-blind peer review) following acceptance by the journal’s Scientific/Editorial Committee.
References
Butera F., De Michelis G. (2024), Intelligenza artificiale e lavoro, una rivoluzione governabile, Venezia, Marsilio
Cetindamar D., Kitto K., Wu M., Zhang Y., Abedin B., Knight S. (2024), Explicating AI literacy of employees at digital workplaces, IEEE Transactions on Engineering Management, 71, pp.810-823
Clerici C.A., Chopard S., Levi G. (2024), Ammalarsi di una patologia rara in tempi di intelligenza artificiale, Recenti progressi in medicina,115, n.2, pp.67-75
Grenci S.B. (2024), Artificial Intelligence Applications to Support the Automation of the Administrative. Procedure | Le Applicazioni Di Intelligenza Artificiale a supporto dell’automazione del Procedimento amministrativo, Rivista italiana di informatica e diritto: periodico internazionale del CNR-IGSG, 6, n.1, (web)
McCarthy J., Minsky M., Rochester N., Shannon C. (1955) A proposal for Dartmouth summer research project on artificial intelligence, AI Magazine, 27, n.4, pp.12-14
Martire, D. (2024), Human in the Loop. The Human Being as a Conditioning Factor of – or Conditioned by – Artificial Intelligence, Rivista italiana di informatica e diritto. Periodico internazionale del CNR-IGSG, 6, n.2, (web)
Markauskaite L., Marrone R., Poquet O., Knight S., Martinez-Maldonado R., Howard S., Tondeur J., de Laat M., Buckingham Shum S., Gašević D., Siemens G. (2022), Rethinking the entwinement between artificial intelligence and human learning. What capabilities do learners need for a world with AI?, Computers & Education: Artificial Intelligence, n.3, 100056
Milana M., Brandi U., Hodge S., Hoggan-Kloubert T. (2024), Artificial intelligence (AI), conversational agents, and generative AI: implications for adult education practice and research, International Journal of Lifelong Education, 43, n.1, pp.1-7
Ng D.T.K., Leung J.K.L., Chu S.K.W., Qiao M.S. (2021), Conceptualising AI literacy: An exploratory review, Computers & Education. Artificial Intelligence, n.2, 100041
Peruzzi M. (2024), La discriminazione algoritmica, Equal. Rivista di Diritto Antidiscriminatorio, n.1, pp.7-23
Pollicino O., Dunn P. (2024), Disinformazione e intelligenza artificiale nell’anno delle global elections: rischi (ed opportunità), Federalismi.it. Rivista di Diritto Pubblico Italiano, comparato, europeo, n.12, pp.iv-xxiii
Rahimi F., Talebi Bezmin Abadi A. (2023), Passive contribution of ChatGPT to scientific papers, Annals of Biomedical Engineering, 51, n.11, pp.2340-2350
Rice S., Crouse S. R., Winter S. R., Rice C. (2024), The advantages and limitations of using ChatGPT to enhance technological research, Technology in Society, 76, 102426
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