Artificial intelligence, employment and social conflict in Kathmandu medical college hospital, Nepal

Abstract

The concept of consciousness, once considered uniquely human, is increasingly manifested in machines through technological evolution, culminating in advanced AI systems (Russell & Norvig, 2021). Rule-based AI executes tasks under direct human instruction, whereas expert AI exhibits adaptive, near-sentient behavior, challenging traditional human-centered production frameworks (Nilsson, 2014). As AI advances, physical, mental, and skilled labor face creation, replacement, and displacement, contributing to rising unemployment and social conflict (Brynjolfsson & McAfee, 2014). A study of 119 AI systems at KMC Hospital, involving 1,214 employees, revealed that expert AI significantly drives labor displacement, reshaping employment dynamics and societal structures (Frey & Osborne, 2017) in Nepal. Humans distinguished themselves through the cognitive revolution, enabling tool use and laying the foundation for modernization and the AI-driven Fourth Industrial Revolution (Schwab, 2016). As machines perform cognitive-like tasks, the relevance of human labor is increasingly questioned. Mechanization now extends beyond routine work to skilled labor, encompassing creation, replacement, and displacement, thereby threatening physical, mental, and skilled labor (Acemoglu & Restrepo, 2019). This labor disruption challenges the legacy of the 12,000-year-old agricultural revolution. Globally, job losses are rising, and in Nepal, hospitals implementing AI have improved service efficiency while reducing employment, highlighting emerging social conflicts around labor (Kshetri, 2021) in hospitals. This study employed a post-positivist approach to move beyond the objectivity of positivism and examine the subjective, multi-dimensional impacts of AI on employment and emerging social conflicts (Phillips & Burbules, 2000). Focusing on transformations induced by rule-based and expert AI, the research analyzed their effects on physical, mental, and skilled labor through creation, replacement, and displacement. Using a qualitative design, the study examined 119 AI tools at KMC Hospital, collecting data from 19 respondents among 1,214 employees across eight departments via structured face-to-face and phone interviews. Data were analyzed descriptively and analytically to draw conclusions about AI-driven labor dynamics. AI tools of 119 were identified, primarily classified as rule-based or expert systems (Russell & Norvig, 2021). Expert AI was mostly used in health care, pathology, and radiology for complex and expert tasks; however, rule-based AI was chiefly vii deployed in administration, security, housekeeping, driving, and pharmacy. AI can effectively deliver an extent of management that is beyond human capability, increasing throughput, quality of service, reliability, and operational efficiency. Across the eight departments, AI adoption has increased steadily, with robotic systems simplifying workflows and administrative processes. These developments are a clear indication of the sphere of AI and, even more so, efficient delivery metrics in each department's responsibilities. Employees number of 1214 across eight departments engage with rule-based or expert AI tools, revealing patterns of job creation, replacement, and displacement (Brynjolfsson & McAfee, 2014). Rule-based AI primarily supports job creation and replacement, whereas expert AI drives displacement. For instance, robotic AI can complete tasks that initially needed four workers with one, and pathology AI can process at a rate of 900 samples an hour, which previously took two days. As a result, workers doing physical, mental, and skilled labor will be at a greater risk of unemployment, creating greater social inequality and causing an increase in social conflict as AI is altering the landscape of work and employment. AI tools about 119, primarily rule-based and expert systems are in active use across eight departments (Russell & Norvig, 2021). Rule-based AI predominates in administration, security, and pharmacy, supporting job creation and replacement, whereas expert AI dominates healthcare, pathology, and radiology, contributing to displacement. Recent additions, including robotic AI and 64-channel MRI machines, have transformed workflows, enabling tasks previously requiring multiple workers to be completed by one and pathology processing in one hour instead of two days. Across 1,214 roles, physical, mental, and skilled labor are increasingly displaced, shifting employment conflicts from institutional to broader societal levels.