Rola SI w wspieraniu zdrowia, dobrostanui efektywności pracownika

The article "Embracing Real AI: A Call to Action for Medical Physicists in Healthcare" urges medical physicists to prepare for the integration of artificial intelligence (AI) into healthcare practices, emphasizing their pivotal role in adapting to technological advancements. The authors advocate for embracing AI through advocacy, broadening perspectives, and enhancing coordination and communication. They propose an ABC strategy focusing on increasing educational initiatives, fostering interdisciplinary collaboration, and creating team collaboration to facilitate AI integration. The commentary highlights AI's potential in enhancing diagnostics, personalizing medicine, and automating routine tasks while addressing challenges such as data sharing and the role of federated learning. The article calls for medical physicists to lead in embracing AI, emphasizing continuous learning and collaboration to leverage its potential for improving healthcare and patient care. Medical physicists have consistently demonstrated strong interest in developing proficiency in the adoption of new technological advancements. The roots of the profession come from the radiation sciences, including radiation protection, radiation therapy, diagnostic imaging, and nuclear medicine.1 As science and technology continued to evolve, medical physicists' roles have extended into other non-radiation domains, such as non-ionizing-radiation-based imaging (ultrasound and magnetic resonance), molecular imaging, computer aided diagnosis (CAD), information technologies, and data science.2 In addition, medical physicists gradually have adopted increasingly more active roles in ensuring the professional education of other radiology/radiation oncology team members, maintaining high quality standards via quality assurance (QA) methods. They also play a major role in advising the hospital management on medical devices and software acquisition. The continuing expansion of these roles and responsibilities has put medical physicists on the forefront of embracing emerging technologies, making the profession one of the most technical and versatile in healthcare settings. Currently, as our field grows in importance, we medical physicists seek to continue to engage in significant ways to for increased contributions and roles in human health. This commentary/opinion urges medical physicists to prepare for their expanding roles in the field of AI and its implementation and oversight in clinical practice. Medical physicists must embrace "Real AI" to help integrate AI into healthcare practices. Conceptually we advocate for a strategy that involves Real AI through advocacy, broadening, and enhancing coordination/communication (an ABC strategy). In our current and future work medical physicists will use AI to automate routine tasks, allowing medical physicists to focus on more complex tasks. Furthermore, Medical Physics will use AI to enhance efficiency, safety, diagnostic and therapeutic applications, and for personalized medicine. However, as we have done in the past with other complex concepts (such as radiation), medical physicists need to be prepared for the potential risks and ethical dilemmas associated with AI, such as bias and lack of transparency. It will be important that Medical Physicists prepare for the rapidly changing AI landscape, and continue learning, gain hands-on experience, and collaborate with other AI experts in the healthcare environment. This paper aligns with the already approved guidance document developed by the AAPM in conjunction with International Atomic Energy Agency (IAEA)3 that discusses how medical physicists can ensure the effective implementation and management of AI systems. It is crucial for the Clinical Quality Management Program (CQMP) personnel to receive regular training and updates on relevant guidelines and legislation. Clear communication channels should be established with IT experts, vendors, and other stakeholders for smooth coordination.4 Comprehensive documentation should be developed to ensure compliance with contractual obligations and guidelines. The clinical team should be involved in acceptance testing and discussions, depending on the clinical purpose of the AI system.4 Protocols for data collection and curation should be established, along with the development of standardized validation datasets for performance evaluation.4 A system for monitoring updates to AI systems and models should be implemented, with the CQMP leading new acceptance/commissioning rounds for any updates. Lastly, mechanisms for continuous evaluation and improvement of the CQMP processes should be established, which could involve regular audits, feedback mechanisms from end-users, and incorporating lessons learned from previous rounds.4 Nowadays, major healthcare systems in the US consider their data as immensely valuable assets that require rigorous protection to ensure Health Insurance Portability and Accountability Act (HIPAA) compliance, as well as intellectual property considerations. It can be very difficult for researchers to share clinical data with vendors for development purposes without a significant return being specified to the institution, such as joint intellectual property or substantial grant funding. Instead, these healthcare systems encourage their researchers to commercialize their findings independently, allowing the institution to retain full rights to intellectual property. That said, the realization of federated learning would be a significant advancement. To achieve this, a powerful pre-trained model that would be adaptable to operation on different scales and in various clinical scenarios is necessary. It is plausible that local adaptation may not require substantial computing power or AI expertise. This concept is particularly intriguing and could be beneficial to smaller centers and clinics in underserved areas. However, the primary challenge is the cost. As we become more reliant on AI systems like OpenAI's ChatGPT or Google Gemini, we often overlook the fact that these conveniences come with a hefty price tag, costing billions of dollars to develop and maintain.5 As medical physicists we and other healthcare professionals can anticipate that AI will significantly transform healthcare, improving efficiency, accuracy, and the level of detail that can be extracted from imaging, and methods of therapy. These technological advancements are expected to bring immense value to the field, offering a new horizon in diagnostic and therapeutic capabilities. Yet, we also must recognize that it also introduces potential significant risks and ethical dilemmas. One of the primary concerns is the possibility of bias in AI, which can stem from the training data, the algorithms, or their application, leading to potentially detrimental effects on patient care. As medical physicists, we should acknowledge that the complexity and lack of transparency in AI decision-making processes present obstacles in terms of accountability and rectifying errors and requires greater oversight and responsibility. The integration of AI also has great capacity in redefining the role of medical physicists, impacting education and employment within the field. Addressing these issues necessitates the creation of ethical standards for AI in healthcare, emphasizing transparency, responsibility, and equity, with contributions from diverse stakeholders, including patients, medical professionals, and ethicists.6 Such measures are crucial to ensure the responsible utilization of AI in healthcare, and ultimately serve the best interests of patients and society. We anticipate that continued guidance from our professional societies will be helpful as our collective communities develop methods and approaches that help us learn, adopt, and employ AI responsibly. Advocacy: increase educational initiative, public awareness, and recommending processes at all levels of the clinical workforce, as well as patient engagement. Broadening Perspectives: encourage Interdisciplinary Collaborations that allow medical physicists to work with professionals from other disciplines such as computer science, data science, and biomedical engineering, to gain insights into different perspectives on AI applications in healthcare. This enables medical physicists to provide continuing education and connect the community with research opportunities. Improving Coordination and Communication through creating team collaboration: enhance communication with healthcare professionals, administrators, and patients by clearly defining and articulating the role of medical physicists in AI applications. Promote the sharing of knowledge, as exemplified by creating data repositories through contributions, to further creating the foundation of our understanding and application of AI in the field. We consider the concept of Real AI in our context to be aimed at providing and/or qualifying a ready AI product that has undergone a rigorous QA process, that is free of false additives and biases, with data carefully curated to represent the demographics and be attuned to the needs of the clinic, sourced with proper ingredients, and abiding by laws and regulations that can ensure the product serves the common health needs of patients and benefits the public's interest. What AI 'is' and what it 'is not' is a complex topic that warrants further exploration and understanding, but one vital for comprehension of what utility AI can fulfill in the clinical process, what its advantages and limitations are, and how it can be curated to perform in the clinical scenarios relevant to a particular radiology/radiation oncology practice. Multiple data-analysis algorithms have been created over the course of years, and not all of them qualify as AI.7 What distinction(s) lie in what constitutes AI? One possible interpretation is that AI is a system that can adapt to new data, or a system that generates insights driven by data. AI systems are designed to "learn" and adapt to new data and be stable over the course of introducing data perturbations or employ model adaptation mechanisms. AI systems can adjust the underlying data-processing mechanisms based on the input they receive, which allows them to improve their performance and make more accurate predictions or decisions over time. This is often achieved through techniques such as machine learning, where algorithms are trained on a dataset and then used to make predictions or decisions without being explicitly programed to perform the task.8 Understanding how such datasets are selected, what data needs to be fed into AI model to achieve desired results, and how to prevent common pitfalls and ethical conundrums associated with the use of AI models requires additional training that might yet be lacking in the traditional training of the radiology/radiation oncology adjacent specialists. The scope of involvement of each member of the team when it comes to AI integration into the clinic continues to be determined as the field rapidly evolves. When it comes to the role of medical physicists in conjunction with AI, an open discussion of the exact responsibilities is still ongoing, and feedback is encouraged from all the members of the community. So, what can medical physicists do? They can use AI to enhance quality improvement and safety by analyzing medical data to identify trends, patterns, and outliers.9 This can lead to the identification of areas for improvement or potential safety hazards and help them enter the realm of Responsible AI. AI can also improve diagnostic and therapeutic techniques by enhancing the quality of medical imaging and automating image interpretation.10 Furthermore, AI can help in integrating diagnostics, personalized medicine, and theragnostics by analyzing large datasets to tailor treatment plans to individual patients.11 This can lead to more effective and personalized care. AI can also automate routine tasks in medical physics, such as treatment planning and QA processes, leading to increased efficiency.12 Lastly, AI techniques like machine learning and deep learning can be leveraged for research and development to analyze complex datasets, discover patterns, and develop innovative techniques for disease detection, treatment, and monitoring.13 Whether it involves developing AI-driven solutions like automated segmentation, dose calculations, addressing intricate problems in the clinic, or potentially even contributing to open-source AI initiatives, such activities will empower medical physicists to enhance their skills and make tangible contributions to the advancement of healthcare. Embracing AI not only fosters a sense of accomplishment but also opens doors to the world of `automation' and scaling that will pervade all technologies of the future. The AHAIBC committee is at the center of bringing the medical physicist forward by developing curriculum concepts, bootcamps, and engendering engagement for our society. Integration of AI into the realm of medical physics education is critical, especially considering the potential significance of incorrect AI usage or misapplication. The physicist is responsible for installing and commissioning the AI software, ensuring the modeling is not biased, performing continuing QA on the hospital data and processes, and establishing efficient resource management. Embracing education in AI offers new benefits for medical physicists as it is already revolutionizing various industries and professional practices and we need to be equally prepared. One way to engage and prepare healthcare professionals for the upcoming AI wave is to start with the roots of quality safety and assurance. To do this, we should enable a comprehensive QA program that encompasses all clinical operations related to medical fields including radiology, nuclear medicine, and radiation oncology. Ensuring the safe operation of hardware, software, clinical operation processes and machinery is of utmost importance and one of the most crucial responsibilities of a medical physicist. A Real AI approach can be highly beneficial in achieving the goal of safe clinical implementation. Understanding the potential and limitations of AI serves as a cornerstone for fostering engagement not only within our profession but with other healthcare providers. Continuous learning and participation in hands-on experience are essential components for navigating the complexities of AI applications within healthcare. Collaboration, networking, and exploring AI's purpose and impact are equally vital in this journey. Additionally, some physicists may choose personal projects, embracing challenges in small groups, and actively contributing to AI-focused teams to amplify the motivation and expertise of our field. Insights through personal and collaborative opportunities ultimately provide for and encourage professional growth and innovation within our medical physics field. Some medical physicists may be able to attend specialty meetings and conferences dedicated to AI which further enriches their knowledge base and provides them avenues for fruitful collaboration. There are successful educational programs such as the Radiological Society of North America Artificial Intelligence (RSNA AI)-certificate program.14 Interdisciplinary cooperation and inter-institutional collaboration for AI experts is of paramount importance for integrating AI into medical physicists' practice on a larger scale, and mechanisms enabling this collaboration should be provided to the community. In summary, the authors believe that being prepared for and embracing the changes that AI is already bringing at the current time will benefit our community, healthcare, patient care, and society at large immediately and for the future. We are at a critical juncture, which can be considered a fourth industrial revolution, where AI and automation are applied more broadly. Medical physicists have a pivotal role to play in this revolution. We need to position ourselves at the forefront of 'Real AI' and lead the charge in this exciting new era. It is time for action, and we can take the first steps with potentially just a few ABCs. All authors contributed their efforts in writing and editing this call for action. ChatGPT search engine has been utilized to provide additional background to the subject of matter for illustrative purposes. The authors appreciate members of the Ad. The authors declare no conflicts of interest. The content for this call for action has been edited with the help of large language models ChatGPT and Google NotebookLM.

Occupational stress research has consistently documented significant relationships between work characteristics and employee mental health, with the components of the Demand-Control-Support (DCS) model of occupational stress (i.e., job demand, job control and social support) being widely used to capture work characteristics (Karasek & Theorell, 1990). The work characteristics of the DCS model appear to be key determinants of employee health outcomes for individuals in a diverse range of occupations (de Lange, Taris, Kompier, Houtman, & Bongers, 2003; van der Doef & Maes, 1999). Although previous research suggests that the DCS model may provide a strong foundation for investigating the effects of the work environment on employee health, the model does not, however, consider the possible effects of the broader organisational context on the health of employees. In light of this criticism, a growing body of research in the last decade (Fujishiro & Heaney, 2007) has begun to compare the influence of work characteristics, as measured by the DCS model, with a concept known as 'organisational justice' in order to broaden the focus of the DCS model and to account for the organisational environment (e.g. Elovainio, Kivimaki, & Vahtera, 2002; Kivimaki, et al., 2005; Kivimaki, et al., 2004; Zohar, 1995). Preliminary results from studies that have compared the effects of work characteristics and organisational justice onto health outcomes tend to indicate that justice is a stronger antecedent of employee mental health than work characteristics. Research studies incorporating both work characteristics and justice are relatively few however and uncertainty remains regarding the relative importance of justice. The lack of clarity as to whether organisational justice captures greater variation in employee health outcomes than work characteristics may be due, at least in part, to methodological issues in previous studies.;In particular, previous justice research that has controlled for the effects of the DCS components have not tended to include the full complement of organisational justice types and have often assumed direct or linear relationships between antecedents and health outcomes. In order to provide some clarity regarding the importance of organisational justice, this thesis seeks to compare the predictive ability of work characteristics and organisational justice in terms of mental health among a sample of police officers. The possibility of curvilinear relationships and/or interaction effects will be considered and the four empirically distinct types of organisational justice will each be included as antecedents to offer transparency about how different fairness perceptions affect employee mental health. The relationships between three mental health indicators will also be investigated and the possible prevalence of work related depression in Australia will be explored. Data for this thesis was obtained in 2005 and 2006 as part of a larger ARC project. Hierarchical multiple regression analyses found that job demands were significantly related to mental health outcomes at both baseline and one year follow-up. The effects of job level resources, in the form of job control and social support, were largely limited to concurrent mental health. Similarly, the unique effects of organisational justice were restricted to short term mental health, particularly job satisfaction. Therefore, although there is some potential for organisational justice to promote employee mental health, the effects of job demands on employee health appear to be considerably more important. Ongoing monitoring of job demands, including the number of demands faced and time pressures, may avoid a situation where employee mental health is compromised.;Without preventative health promotion strategies, the consequences of job demands may have adaptation effects that are detrimental to employee mental health.

Introduction: Occupational stress is one of the most important phenomena in the workplace. Mental health, as an important factor in all personal, social, and occupational life aspects, is one of the areas of interest in mental health. The purpose of this study was to determine the relationship between job stress and mental health among workers of central iron ore in Bafgh City, Iran.
 Methods: This was a descriptive correlational study. The statistical population included 2400 people (1600 workers and 800 employees) working in central Bafgh iron ore in 2014. The sample size was estimated as 331 using the Morgan and Krejcie table. Approximately, 216 persons were employed in the labor sector and 115 in the employment sector. Data collection tools included the Goldberg general health questionnaire (GHQ) and Depression Anxiety Stress Scales (DASS). The DASS was designed in three parts. The first part included demographic data (7 items). The second section had 28 questions and dealt with the participants' general health status. The third part included 42 questions and was about occupational stress. Later, Spss21 was used and Pearson correlation coefficient and regression tests were run to analyze the data and test the hypotheses.
 Results: The results showed that mental health was higher in employees, while job stress was higher among the workers. This indicated a significant association between job stress and mental health in both employees and workers. However, regarding the demographic variables, only gender was related to mental health and rest of the demographic variables had no significant relationship with mental health and job stress (5≥participants).
 Conclusion: Occupational stress was related to mental health in employees and workers; this can affect the level of production and product quality. Furthermore, occupational tress and mental health not only affect the workers and employees, but also expose the society and other people at risk. Consequently, it is necessary to plan and render different services at the occupation environment to decrease job stress and improve the current situation.

Poor physical and mental health in employees can result in a serious loss of productivity. Early detection and management of unhealthy behaviours and mental health symptoms can prevent productivity loss and foster healthy workplaces. To examine health-related behaviours, mental health status and help-seeking patterns in employees, across different industries in Hong Kong. Participants were telephone-interviewed and assessed using the Case-finding and Help Assessment Tool (CHAT) with employee lifestyle risk factors, mental health issues and help-seeking intentions screened across eight industries. Subsequent data analysis involved descriptive statistics and chi-square tests. There were 1031 participants. Key stressors were work (30%), family (19%), money (14%) and interpersonal issues (5%). Approximately 18, 9 and 9% of participants were smokers, drinkers and gamblers, respectively, and only 51% exercised regularly. Depressive and anxiety symptoms were reported by 24 and 31% of employees, respectively. Issues for which they wanted immediate help were interpersonal abuse (16%), anxiety (15%), anger control (14%) and depression (14%). Employees with higher educational attainment were less likely to smoke, drink and gamble than those with lower attainment. Lifestyle and mental health status were not associated with income. Employees in construction and hotel industries smoked more and those in manufacturing drank more than those in other industries. Physical and mental health of Hong Kong employees are concerning. Although employee assistance programmes are common among large companies, initiation of proactive engagement approaches, reaching out to those employees in need and unlikely to seek help for mental health issues, may be useful.

The present study investigated relationships among self-management skills, communication with superiors, and the mental health of employees in a Japanese worksite. The subjects were manufacturing workers in a medium-sized company in Kyushu. In 1999, we mailed a self-administrated questionnaire which included questions on age, gender, job rank, communication with superiors, a General Self-Efficacy Scale, a Self-Management Skill Scale, and the Japanese version of the 12-item General Health Questionnaire (GHQ-12). Eighty percent of the subjects returned the questionnaire. Excluding senior managers and insufficient answers, the final response rate was fifty-five percent. The multiple regression analysis showed that job rank contributed significantly and positively and that age, communication with superiors, and self-management skills contributed significantly and negatively to the GHQ-12. Our results implied that self-management skills might have the potential of affecting the mental health of Japanese employees.

IntroductionThe COVID-19 pandemic caused employment related challenges worldwide. Adults diagnosed with Autism Spectrum Disorders (ASD) are especially vulnerable, due to pre-existing employment challenges, intolerance to changes and uncertainty and high levels of related anxiety.ObjectivesTo examine COVID-19 related changes in work experiences and mental health of employees with ASD who held a steady job before the COVID-19 outbreak.Methods Data were collected from 23 participants diagnosed with ASD (4 females), aged 20–49, who answered an online administered survey at two timepoints: prior to the COVID-19 outbreak, and during the outbreak. Self-reports included measures of background and employment status; mental health (General Health Questionnaire-12); job satisfaction (Minnesota Satisfaction Questionnaire); and satisfaction of psychological needs at work (Psychological Need Satisfaction and Frustration – Work domain).Results Participants who continued to physically attend work maintained pre-COVID-19 levels on all assessed variables. Participants who transitioned to remote work from home preserved their salary levels and job satisfaction, but showed a marginally significant deterioration in mental health and a significant decrease in the satisfaction of their needs for competence and autonomy at work. Unemployed participants showed a significant decrease in mental health.Conclusions Results highlight employment as a protective factor from the potential negative implications of COVID-19 on mental-health of employees with ASD. Employees who transition to working from home require personalized work-support plans due to the possible negative effects of this transition on mental health. Maintaining the routine of physically reporting to work should be preferred, when possible.

The efforts would be on how Artificial Intelligence be essential in the well-being of employees and Human Resource department in a consortium and therefore why such department should do planning to adopt Artificial Intelligence and what conceivable the role and challenges for the employees in the area of Human Resource and why it has become important to understand about Artificial Intelligence would be answered by highlighting the motivation for Artificial Intelligence, Artificial Intelligence can be adopted for any department but this paper try to emphasize on what Artificial Intelligence can contribute in Employee well-being. Artificial Intelligence is transforming Human Resource Management by enhancing employee wellness and optimizing workforce management. Artificial Intelligence-driven tools help organizations improve Recruitment, Performance Evaluation, Employee Engagement, and overall, Job satisfaction. In employee well-being, Chatbots and virtual assistants driven by Artificial Intelligence provide real-time mental health support, while sentiment analysis tools assess workplace morale by analyzing employee feedback. Wearable technology and Artificial Intelligence -driven wellness programs further aid in stress management and Work-Life balance. In Human Resource Management, Artificial Intelligence streamlines talent acquisition through automated resume screening, predictive analytics for candidate selection, and bias reduction in hiring. Artificial Intelligence -powered learning platforms personalize training programs, boosting employee skill development. Furthermore, Artificial Intelligence enhances workforce analytics by identifying trends in employee performance, absenteeism, and attrition risks, allowing Human Resource professionals to implement proactive strategies. Despite its benefits, Artificial Intelligence adoption in Human Resource Management uplift concerns about data safety, algorithmic bias, and ethical considerations in decision-making. Organizations must ensure transparency and fairness while integrating Artificial Intelligence into Human Resource processes. The upcoming era of Artificial Intelligence in Human Resource Management lies in generating a balanced approach that leverages Artificial Intelligence’s analytical power while maintaining the human touch in employee interactions. By fostering a data-driven, employee-centric approach, Artificial Intelligence contributes to a more engaged, healthier, and productive workforce, ultimately leading to improved organizational performance and job satisfaction.

The rapid integration of artificial intelligence (AI) into modern U.S. workplaces presents unprecedented challenges and opportunities for employee mental health and wellbeing. This comprehensive review examines the intricate relationships between leadership practices, organizational culture, and employee psychological wellbeing as organizations navigate AI adoption and its complexities. Drawing from extensive research spanning organizational psychology, technology management, and occupational health, this article synthesizes findings on how AI-driven workplace transformations affect psychological safety, job burnout, role clarity, and employee stress. The analysis reveals that leadership behaviors and organizational culture serve as critical mediators between AI implementation and employee mental health outcomes, with psychological safety emerging as a fundamental prerequisite for successful AI integration. The Job Demands-Resources model provides a valuable framework for understanding how AI-related workplace changes contribute to both wellbeing and burnout. Evidence suggests that proactive, human-centered approaches to AI adoption, when properly implemented with strong leadership support, can significantly improve employee mental health outcomes. However, significant challenges remain in managing AI-related anxieties, skill displacement concerns, algorithmic management stress, and the ethical complexities of AI-augmented work environments.

Based on previous research results that recognized the role of paternalistic leadership in promoting a positive work climate, this study explored the impact of a toxic work environment on the mental health and well-being of employees. We used the quantitative methodology to collect and analyze data. A sample of 108 participants from Iraqi internet service provider (ISPs) companies represented the purposive study sample. We targeted employees who experienced the COVID-19 pandemic. All data was collected through an electronic questionnaire (Google and Microsoft Forms). The research model was tested using structural equation modeling (SEM). The results showed a negative effect of the toxic workplace on the mental health of employees. This also had a negative impact on their well-being. The results also indicated that paternalistic leadership has a positive effect on reducing the impact of toxic workplace on employees' mental health. This role was more apparent in modifying the negative relationship between mental health problems and employee well-being. The results showed that workplace bullying, in particular, is less affected by paternalistic leadership practices.

The application of artificial intelligence (AI) to judicial decision-making has already begun in many jurisdictions around the world. While AI seems to promise greater fairness, access to justice, and legal certainty, issues of discrimination and transparency have emerged and put liberal democratic principles under pressure, most notably in the context of bail decisions. Despite this, there has been no systematic analysis of the risks to liberal democratic values from implementing AI into judicial decision-making. This article sets out to fill this void by identifying and engaging with challenges arising from artificial judicial decision-making, focusing on three pillars of liberal democracy, namely equal treatment of citizens, transparency, and judicial independence. Methodologically, the work takes a comparative perspective between human and artificial decision-making, using the former as a normative benchmark to evaluate the latter.The chapter first argues that AI that would improve on equal treatment of citizens has already been developed, but not yet adopted. Second, while the lack of transparency in AI decision-making poses severe risks which ought to be addressed, AI can also increase the transparency of options and trade-offs that policy makers face when considering the consequences of artificial judicial decision-making. Suchtransparency of optionsoffers tremendous benefits from a democratic perspective. Third, the overall shift of power from human intuition to advanced AI may threaten judicial independence, and with it the separation of powers. While improvements regarding discrimination and transparency are available or on the horizon, it remains unclear how judicial independence can be protected, especially with the potential development of advanced artificial judicial intelligence (AAJI). Working out the political and legal infrastructure to reap the fruits of artificial judicial intelligence in a safe and stable manner should become a priority of future research in this area.

Introduction Author Arthur C. Clarke famously argued that in science fiction literature “any sufficiently advanced technology is indistinguishable from magic” (Clarke). On 30 November 2022, technology company OpenAI publicly released their Large Language Model (LLM)-based chatbot ChatGPT (Chat Generative Pre-Trained Transformer), and instantly it was hailed as world-changing. Initial media stories about ChatGPT highlighted the speed with which it generated new material as evidence that this tool might be both genuinely creative and actually intelligent, in both exciting and disturbing ways. Indeed, ChatGPT is part of a larger pool of Generative Artificial Intelligence (AI) tools that can very quickly generate seemingly novel outputs in a variety of media formats based on text prompts written by users. Yet, claims that AI has become sentient, or has even reached a recognisable level of general intelligence, remain in the realm of science fiction, for now at least (Leaver). That has not stopped technology companies, scientists, and others from suggesting that super-smart AI is just around the corner. Exemplifying this, the same people creating generative AI are also vocal signatories of public letters that ostensibly call for a temporary halt in AI development, but these letters are simultaneously feeding the myth that these tools are so powerful that they are the early form of imminent super-intelligent machines. For many people, the combination of AI technologies and media hype means generative AIs are basically magical insomuch as their workings seem impenetrable, and their existence could ostensibly change the world. This article explores how the hype around ChatGPT and generative AI was deployed across the first six months of 2023, and how these technologies were positioned as either utopian or dystopian, always seemingly magical, but never banal. We look at some initial responses to generative AI, ranging from schools in Australia to picket lines in Hollywood. We offer a critique of the utopian/dystopian binary positioning of generative AI, aligning with critics who rightly argue that focussing on these extremes displaces the more grounded and immediate challenges generative AI bring that need urgent answers. Finally, we loop back to the role of schools and educators in repositioning generative AI as something to be tested, examined, scrutinised, and played with both to ground understandings of generative AI, while also preparing today’s students for a future where these tools will be part of their work and cultural landscapes. Hype, Schools, and Hollywood In December 2022, one month after OpenAI launched ChatGPT, Elon Musk tweeted: “ChatGPT is scary good. We are not far from dangerously strong AI”. Musk’s post was retweeted 9400 times, liked 73 thousand times, and presumably seen by most of his 150 million Twitter followers. This type of engagement typified the early hype and language that surrounded the launch of ChatGPT, with reports that “crypto” had been replaced by generative AI as the “hot tech topic” and hopes that it would be “‘transformative’ for business” (Browne). By March 2023, global economic analysts at Goldman Sachs had released a report on the potentially transformative effects of generative AI, saying that it marked the “brink of a rapid acceleration in task automation that will drive labor cost savings and raise productivity” (Hatzius et al.). Further, they concluded that “its ability to generate content that is indistinguishable from human-created output and to break down communication barriers between humans and machines reflects a major advancement with potentially large macroeconomic effects” (Hatzius et al.). Speculation about the potentially transformative power and reach of generative AI technology was reinforced by warnings that it could also lead to “significant disruption” of the labour market, and the potential automation of up to 300 million jobs, with associated job losses for humans (Hatzius et al.). In addition, there was widespread buzz that ChatGPT’s “rationalization process may evidence human-like cognition” (Browne), claims that were supported by the emergent language of ChatGPT. The technology was explained as being “trained” on a “corpus” of datasets, using a “neural network” capable of producing “natural language“” (Dsouza), positioning the technology as human-like, and more than ‘artificial’ intelligence. Incorrect responses or errors produced by the tech were termed “hallucinations”, akin to magical thinking, which OpenAI founder Sam Altman insisted wasn’t a word that he associated with sentience (Intelligencer staff). Indeed, Altman asserts that he rejects moves to “anthropomorphize” (Intelligencer staff) the technology; however, arguably the language, hype, and Altman’s well-publicised misgivings about ChatGPT have had the combined effect of shaping our understanding of this generative AI as alive, vast, fast-moving, and potentially lethal to humanity. Unsurprisingly, the hype around the transformative effects of ChatGPT and its ability to generate ‘human-like’ answers and sophisticated essay-style responses was matched by a concomitant panic throughout educational institutions. The beginning of the 2023 Australian school year was marked by schools and state education ministers meeting to discuss the emerging problem of ChatGPT in the education system (Hiatt). Every state in Australia, bar South Australia, banned the use of the technology in public schools, with a “national expert task force” formed to “guide” schools on how to navigate ChatGPT in the classroom (Hiatt). Globally, schools banned the technology amid fears that students could use it to generate convincing essay responses whose plagiarism would be undetectable with current software (Clarence-Smith). Some schools banned the technology citing concerns that it would have a “negative impact on student learning”, while others cited its “lack of reliable safeguards preventing these tools exposing students to potentially explicit and harmful content” (Cassidy). ChatGPT investor Musk famously tweeted, “It’s a new world. Goodbye homework!”, further fuelling the growing alarm about the freely available technology that could “churn out convincing essays which can't be detected by their existing anti-plagiarism software” (Clarence-Smith). Universities were reported to be moving towards more “in-person supervision and increased paper assessments” (SBS), rather than essay-style assessments, in a bid to out-manoeuvre ChatGPT’s plagiarism potential. Seven months on, concerns about the technology seem to have been dialled back, with educators more curious about the ways the technology can be integrated into the classroom to good effect (Liu et al.); however, the full implications and impacts of the generative AI are still emerging. In May 2023, the Writer’s Guild of America (WGA), the union representing screenwriters across the US creative industries, went on strike, and one of their core issues were “regulations on the use of artificial intelligence in writing” (Porter). Early in the negotiations, Chris Keyser, co-chair of the WGA’s negotiating committee, lamented that “no one knows exactly what AI’s going to be, but the fact that the companies won’t talk about it is the best indication we’ve had that we have a reason to fear it” (Grobar). At the same time, the Screen Actors’ Guild (SAG) warned that members were being asked to agree to contracts that stipulated that an actor’s voice could be re-used in future scenarios without that actor’s additional consent, potentially reducing actors to a dataset to be animated by generative AI technologies (Scheiber and Koblin). In a statement issued by SAG, they made their position clear that the creation or (re)animation of any digital likeness of any part of an actor must be recognised as labour and properly paid, also warning that any attempt to legislate around these rights should be strongly resisted (Screen Actors Guild). Unlike the more sensationalised hype, the WGA and SAG responses to generative AI are grounded in labour relations. These unions quite rightly fear the immediate future where human labour could be augmented, reclassified, and exploited by, and in the name of, algorithmic systems. Screenwriters, for example, might be hired at much lower pay rates to edit scripts first generated by ChatGPT, even if those editors would really be doing most of the creative work to turn something clichéd and predictable into something more appealing. Rather than a dystopian world where machines do all the work, the WGA and SAG protests railed against a world where workers would be paid less because executives could pretend generative AI was doing most of the work (Bender). The Open Letter and Promotion of AI Panic In an open letter that received enormous press and media uptake, many of the leading figures in AI called for a pause in AI development since “advanced AI could represent a profound change in the history of life on Earth”; they warned early 2023 had already seen “an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control” (Future of Life Institute). Further, the open letter signatories called on “all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4”, arguing that “labs and independent experts should use this pause to jointly develop and implement a set of shared safety protocols for advanced AI design and development that are rigorously audited and overseen by independent outside experts” (Future of Life Institute). Notably, many of the signatories work for the very companies involved in the “out-of-control race”. Indeed, while this letter could be read as a moment of ethical clarity for the AI industry, a more cynical reading might just be that in warning that their AIs could effectively destroy the w

The rapid adoption of artificial intelligence (AI) on the web platform across multiple sectors has highlighted not only its inherent technical hurdles, such as unpredictability and lack of transparency, but also significant societal concerns. These include the misuse of AI technology, invasions of privacy, discrimination fueled by biased data, and infringements of copyright. Such challenges jeopardize the sustainable growth of AI and risk the erosion of societal trust, industry adoption and financial investment. This analysis explores the AI system’s lifecycle, emphasizing the essential continuous monitoring and the need for creating trustworthy AI technologies. It advocates for an ethically oriented development process to mitigate adverse effects and support sustainable progress. The dynamic and unpredictable nature of AI, compounded by variable data inputs and evolving distributions, requires consistent model updates and retraining to preserve the integrity of services. Addressing the ethical aspects, this paper outlines specific guidelines and evaluation criteria for AI development, proposing an adaptable feedback loop for model improvement. This method aims to detect and rectify performance declines through prompt retraining, thereby cultivating robust, ethically sound AI systems. Such systems are expected to maintain performance while ensuring user trust and adhering to data science and web technology standards. Ultimately, the study seeks to balance AI’s technological advancements with societal ethics and values, ensuring its role as a positive, reliable force across different industries. This balance is crucial for harmonizing innovation with the ethical use of data and science, thereby facilitating a future where AI contributes significantly and responsibly to societal well-being.

Background: Occupational safety in this context refers to efforts to prevent accidents and protect the physical integrity of employees, while occupational health includes efforts to prevent work-related diseases and maintain the physical and mental health of employees. The purpose of this writing is to find out the relationship between the level of occupational safety and health in the company and employee performance Method: The research method used is a literature review. Researchers collect various sources of information, such as scientific journals, books and other publications related to occupational safety and health levels and employee performance. Results: Occupational safety and health (K3) have a very important role in the work environment, with occupational safety aimed at preventing accidents and protecting the physical integrity of employees, while occupational health includes efforts to prevent work-related diseases and maintain the physical and mental health of employees. Holistic and comprehensive K3 management is an important key in improving employee performance, where a good combination of occupational safety and health has a greater impact on employee performance. The implications of this research are important for company management, because investing in K3 is not only a social responsibility but also a business strategy that can increase company productivity and sustainability. Management needs to prioritize K3 in business strategy, paying attention to supporting K3 factors such as a strong safety culture, quality K3 training, and strict work environment monitoring, to create a healthy, safe and productive work environment.

The application of Artificial Intelligence (AI) in supply chains (SCs) has grown significantly in recent years. AI has been used in the SCs for various purposes such as optimizing inventory management, predicting demand, forecasting, and automated decision-making. Furthermore, AI can have significant impacts on the supply chain, such as better customer service, reduced inventory costs, improved forecasting accuracy, and more efficient processes. Despite these potential benefits, there are also risks associated with AI, such as data privacy and security issues, lack of transparency, and potential bias. In addition, there are still gaps related to the lack of understanding of AI capabilities and the lack of data for AI models to use in decision-making. Therefore, this study presents an overview of the applications of AI in SCs, highlighting the gaps and impacts of its use in the industry. While AI offers many advantages to supply chain management, some gaps need to be addressed. First, AI needs to be integrated with existing supply chain systems and needs to be trained to make accurate predictions. AI algorithms are only as good as the data they are given, so it is important to ensure that the data is accurate and reliable. Secondly, AI needs to be able to scale to handle the increasing complexity of the supply chain. The findings of this study suggested that the impact of AI in SCs is significant. AI can reduce costs, increase efficiency, and improve customer satisfaction. AI can also reduce the time needed to complete tasks, allowing the supply chain to be more agile and responsive to changes in market conditions. Therefore, this study provides an overview of the current applications of AI in SCs, identifies gaps and opportunities for further development, and discusses the potential impacts of AI onthe supply chain

The present study investigated the relationship between work-related burnout, spiritual bypass, and mental health in employees. The purpose of the study is to find out the mediating role of Spiritual bypass between work-related burnout and mental health in employees. The total sample consists of 180 employees including men (102) and women (78) from the private and government sector, which are selected through non-probability purposive sampling. The data was collected in person and the work-related burnout subscale from the Copenhagen Burnout Inventory, the Depression, Anxiety and Stress Scale and the Spiritual Bypass Scale were used. Results of the Pearson Product Moment correlation reveal that work-related burnout is negatively related to mental health as well as spiritual bypass. Spiritual bypass was also found to be positively related to mental health. Moreover, spiritual bypass plays a mediating role between work-related burnout and mental health in employees. This study highlights the implications for employees, workers in private institutions, and private business owners. Focusing on the Pakistani workforce, the study highlights the role of spiritual bypass in a context where spirituality often intersects with personal and professional life. Furthermore, this is one of the first studies to empirically establish the mediating role of spiritual bypass between work-related burnout and mental health, as per our knowledge. Thirdly, the inclusion of participants from both private and governmental sectors ensures the findings are relevant to diverse organisational settings. Lastly, this study highlights the interventions required for mental health practices needed in the workplace as well as how spiritual bypass can be used positively.