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Cognitive automation powered by advanced intelligent technologies is increasingly enabling organizations to automate more of their knowledge work tasks. Although this often offers higher efficiency and lower costs, cognitive automation exacerbates the erosion of human skill and expertise in automated tasks. Accepting the erosion of obsolete skills is necessary to reap the benefits of technology—however, the erosion of essential human expertise is problematic if workers remain accountable for tasks for which they lack sufficient understanding, rendering them incapable of responding if the automation fails. Though the phenomenon is widely acknowledged, the dynamics behind such undesired skill erosion are poorly understood. Thus, taking the perspective of sociotechnical systems, we conducted a case study of an accounting firm that had experienced skill erosion over a number of years due to reliance on their software’s automated functions. We synthesized our findings using causal loop modeling based on system dynamics. The resulting dynamic model explains skill erosion via an interplay between humans’ automation reliance, complacency, and mindful conduction. It shows how increasing reliance on automation fosters complacency at both individual and organizational levels, weakening workers’ mindfulness across three work task facets (activity awareness, competence maintenance, and output assessment), resulting in skill erosion. Such skill erosion may remain obscure, acknowledged by neither workers nor managers. We conclude by discussing the implications for theory and practice and identifying directions for future research.

This paper explores the ambiguous impact of new information and communications technologies (ICTs) on the cultivation of moral skills in human beings. Just as twentieth century advances in machine automation resulted in the economic devaluation of practical knowledge and skillsets historically cultivated by machinists, artisans, and other highly trained workers (Braverman 1974 ), while also driving the cultivation of new skills in a variety of engineering and white collar occupations, ICTs are also recognized as potential causes of a complex pattern of economic deskilling, reskilling, and upskilling. In this paper, I adapt the conceptual apparatus of sociological debates over economic deskilling to illuminate a different potential for technological deskilling/upskilling, namely the ability of ICTs to contribute to the moral deskilling of human users, a potential that exists alongside rich but currently underrealized possibilities for moral reskilling and/or upskilling. I flesh out this general hypothesis by means of examples involving automated weapons technology, new media practices, and social robotics. I conclude that since moral skills are essential prerequisites for the effective development of practical wisdom and virtuous character, and since market and cultural forces are not presently aligned to bring about the more salutary of the ambiguous potentials presented here, the future shape of these developments warrants our close attention—and perhaps active intervention.

Digitalization adds demands to contend with technological developments for both employees and organizations. At the same time, technological changes transform work to become more intensive and hectic. This study examined determinants of technological well-being after digitized work. Technological well-being was operationalized as Digi-downshifting where decreased workload associates with job satisfaction and as Digi-uplifting where increased workload associates with job satisfaction. A subsample (N = 3321) of workers at digitalized workplaces from the Finnish Quality of Work Life Survey was used in mean comparisons and binary logistic regression analysis. Digi-uplifters emerged as the most predominant profile among categories of technological well-being and ill-being. Extensive working time with technologies and employees’ influencing opportunities at the workplace stood out as the most consistent determinants of technological well-being. Thus, Nordic countries with skilled, technologically oriented workforce and democratic working cultures have particular promise in fostering Digi-uplifting and Digidownshifting at work.

Platform decay is the phenomenon of major internet platforms, such as Google search, Facebook, and Amazon, systematically declining in quality in recent years. This decline in quality is attributed to the particular business model of these platforms and its harms are usually understood to be violations of principles of economic fairness and of inconveniencing users. In this article, we argue that the scope and nature of these harms are underappreciated. In particular, we establish that platform decay constitutes both a cognitive and moral harm to its users. We make this case by arguing that platforms function as cognitive scaffolds or extensions, as understood by the extended mind approach to cognition. It is then a straightforward implication that platform decay constitutes cognitive damage to a platform’s users. This cognitive damage is a harm on its own; however, it can also undermine cognitive capacities that virtue ethicists argue are necessary for developing a virtuous character. We will focus on this claim in regards to the capacity to pay attention, a capacity that platform decay targets specifically. Platform decay therefore also constitutes both cognitive and moral harm, which simultaneously affects billions of people.

The integration of Artificial Intelligence (AI) in healthcare is reshaping clinical practice, offering both opportunities for enhanced decision-making and risks of skill degradation among medical professionals. This growing impact calls for a comprehensive evaluation of its effects on medical expertise. This study presents a mixed-method literature review, combining systematic analysis with narrative synthesis to examine AI-induced deskilling and upskilling inhibition-the erosion of medical expertise and the reduction of opportunities for skill acquisition due to AI-driven decision support systems. Anchoring the discussion in the core medical competencies outlined by the Federation of Royal Colleges of Physicians of the UK-Practical Assessment of Clinical Examination Skills (PACES-MRCPUK), the systematic review identifies key vulnerabilities in physical examination, differential diagnosis, clinical judgment, and physician-patient communication. The narrative review explores broader themes related to Human–AI Interaction and the Impact of AI on Human Skills in Organizations. In response to concerns about the Second Singularity -a scenario in which decision-making autonomy is increasingly ceded to AI, weakening human oversight-this review advocates for a research agenda that prioritizes longitudinal studies, real-time monitoring of AI’s impact, and the development of frameworks to mitigate skill erosion, ensuring the preservation of professional autonomy and the safeguarding of the irreplaceable elements of human judgment in medicine and beyond.

This paper examines how AI at work impacts on-the-job learning, shedding light on workers’ reactions to the groundbreaking AI technology. Based on theoretical analysis, six hypotheses are proposed regarding three aspects of AI’s influence on on-the-job learning. Empirical results demonstrate that AI significantly inhibits people’s on-the-job learning and this conclusion holds true in a series of robustness and endogeneity checks. The impact mechanism is that AI makes workers more pessimistic about the future, leading to burnout and less motivation for on-the-job learning. In addition, AI’s replacement, mismatch, and deskilling effects decrease people’s income while extending working hours, reducing their available financial resources and disposable time for further learning. Moreover, it has been found that AI’s impact on on-the-job learning is more prominent for older, female and less-educated employees, as well as those without labor contracts and with less job autonomy and work experience. In regions with more intense human–AI competition, more labor-management conflicts, and poorer labor protection, the inhibitory effect of AI on further learning is more pronounced. In the context of the fourth technological revolution driving forward the intelligent transformation, findings of this paper have important implications for enterprises to better understand employee behaviors and to promote them to acquire new skills to achieve better human–AI teaming.

Objectives This article aims to evaluate the use and effects of an artificial intelligence system supporting a critical diagnostic task during radiology resident training, addressing a research gap in this field. Materials and methods We involved eight residents evaluating 150 CXRs in three scenarios: no AI, on-demand AI, and integrated-AI. The considered task was the assessment of a multi-regional severity score of lung compromise in patients affected by COVID-19. The chosen artificial intelligence tool, fully integrated in the RIS/PACS, demonstrated superior performance in scoring compared to the average radiologist. Using quantitative metrics and questionnaires, we measured the ‘upskilling’ effects of using AI support and residents’ resilience to ‘deskilling,’ i.e., their ability to overcome AI errors. Results Residents required AI in 70% of cases when left free to choose. AI support significantly reduced severity score errors and increased inter-rater agreement by 22%. Residents were resilient to AI errors above an acceptability threshold. Questionnaires indicated high tool usefulness, reliability, and explainability, with a preference for collaborative AI scenarios. Conclusion With this work, we gathered quantitative and qualitative evidence of the beneficial use of a high-performance AI tool that is well integrated into the diagnostic workflow as a training aid for radiology residents. Critical relevance statement Balancing educational benefits and deskilling risks is essential to exploit AI systems as effective learning tools in radiology residency programs. Our work highlights metrics for evaluating these aspects. Key Points Insights into AI tools’ effects in radiology resident training are lacking. Metrics were defined to observe residents using an AI tool in different settings. This approach is advisable for evaluating AI tools in radiology training. Graphical Abstract

Warangal District, Andhra Pradesh, India, is a key cotton‐growing area in one of the most closely watched arenas of the global struggle over genetically modified crops. In 2005 farmers adopted India’s first genetically modified crop, Bt cotton, in numbers that resemble a fad. Various parties, including the biotechnology firm behind the new technology, interpret the spread as the result of farmer experimentation and management skill, alluding to orthodox innovation‐diffusion theory. However, a multiyear ethnography of Warangal cotton farmers shows a striking pattern of localized, ephemeral cotton seed fads preceding the spread of the genetically modified seeds. The Bt cotton fad is symptomatic of systematic disruption of the process of experimentation and development of management skill. In fact, Warangal cotton farming offers a case study in agricultural deskilling, a process that differs in fundamental ways from the better‐known process of industrial deskilling. In terms of cultural evolutionary theory, deskilling severs a vital link between environmental and social learning, leaving social learning to propagate practices with little or no environmental basis. However, crop genetic modification is not inherently deskilling and, ironically, has played a role in reinvolving farmers in Gujarat in the process of breeding.

Currently, we find ourselves in a paradigm in which we believe that accepting climate change data will lead to a kind of automatic action toward the preservation of our environment. I have argued elsewhere (Fried 2020) that this lack of civic action on climate data is significant when placed in the historical, military context of the technologies that collect this data––Earth remote sensing technologies. However, I have not yet discussed the phenomenological or moral implications of this context, which are deeply interconnected. In this paper, I assert that Earth remote sensing technologies can, if we are not careful, lead us to a kind of environmental deskilling. This assertion comes in four parts. First, the military context of Earth remote sensing technologies––which collect important data on climate change––acts as a kind of stability, as defined by Don Ihde and others. Second, I invoke Sir Patrick Heelan to argue that the theoretical underpinnings of Earth systems science do not translate from military to environmental praxes as we imagine they do. Third, Hannah Arendt makes the case that a state’s trust in simplifying narratives like that of climate data, meant to create “islands” of certainty in an uncertain world, can be self-defeating. That is to say, they can silence public action. I extend these arguments through Vallor’s analysis of moral deskilling, in which she points out that an overemphasis on autonomous data collection––and trust in a kind of automated decision-making on that data––can deskill us from important questions relevant to our collective flourishing. In all of these examples, the lines between environmental and military research are blurry.

In recent years, the technology-driven industrial upgrading in China has resulted in human labor being replaced with robots. This article explores the impact of \"intelligent manufacturing\" on workers from the following two perspectives: labor relations and the labor process. The authors argue that workers on the shopfloor are experiencing some forms of labor degradation due to robotization, i.e., more flexible labor relations, deskilling, and strengthened technical control. Such a corporation-led and machine-centered industrial upgrading is driven by state policy, capital, and the labor market.