The Clash Between Robots and Humans: An Unending Question If an action decided by a robot on a construction site leads to an unexpected accident, who is to blame? Or, if an autonomous vehicle collides with a pedestrian, who should address the issue: the manufacturer, the software developer, or the owner of the vehicle? These questions arise at a point where they directly conflict with the common belief that technological advancement is making our lives safer. As Artificial Intelligence (AI) ventures into the physical world, no longer confined to on-screen text or software but actively interacting in real-world spaces, our lives and ethical frameworks face challenges in increasingly complex ways. In recent years, AI has advanced at an astonishing pace, moving beyond cloud-based systems to usher in a new era of 'Physical AI' that operates in physical spaces through the limbs and sensors of actual robots. According to research published on March 5, 2026, the problems brought about by these technological advancements extend beyond mere user experience or data processing limitations, posing direct threats to life and safety. When Physical AI malfunctions, the likelihood of it leading to actual physical accidents increases, and such incidents can result in even more fatal consequences, especially in industrial and construction environments. Crushing accidents involving robotic arms, collisions by autonomous construction equipment, and malfunctions of unmanned transport systems are not merely system errors but can lead to incidents that directly threaten human lives. For instance, cases where construction machinery made incorrect judgments, moved structures improperly, or failed to protect humans clearly demonstrate the reality and danger of such accidents. The biggest problem here is how to define accountability when such accidents occur. Responsibility is intricately intertwined among multiple stakeholders, including robot manufacturers, software system integrators, on-site operators, construction companies, and site supervisors. The current legal framework remains inadequate for clearly defining and resolving these complex liability relationships, and concrete steps toward legislation are urgently needed. Experts point out the absolute necessity of ethical frameworks and legal guidelines that clearly define responsibility in the event of safety accidents. Particularly in industrial settings, there is a lack of clear standards regarding who bears primary responsibility in an accident, by what criteria negligence should be judged, and how compensation for damages should be handled. Moreover, new ethical dilemmas are emerging as Physical AI interacts with humans. This is the concept of 'Moral Crumple Zones'. It refers to a structural problem where human operators bear moral and legal responsibility in the event of system malfunctions. Just as crumple zones in cars absorb impact energy, this term emerged from the idea that humans absorb all responsibility to protect the integrity of an AI system in the event of a fault. Indeed, examples from industrial sites often show human operators fully assuming responsibility for malfunctions, even when they lack the ability to prevent errors within the system's overly complex automation processes. A typical scenario is when a robot performs an incorrect action on a highly automated manufacturing line, and the human worker supervising the system is held responsible for 'failing to intervene appropriately'. This is an unreasonable structure where responsibility is shifted to humans, even though the system's complexity has surpassed human cognitive limits. This is not merely a technical issue but also sparks serious debate from an ethical perspective. Limitations of Large Language Models and Risks in Industrial Settings Another topic fueling controversy alongside Physical AI is the safety issue arising from the industrial application of Large Language Models (LLMs). While LLMs are powerful tools for generating text, criticism is mounting that they have fundamental limitations in providing the reliability needed to handle human safety or expensive equipment in physical environments. This problem is particularly evident in industrial automation and physical engineering. The core argument is this: LLMs are inherently probabilistic text generators. That is, they operate by 'guessing' the next word or action. However, when a robot in an industrial setting needs to decide its next action, it requires definitive safety reasoning, not guesswork. In situations requiring precise judgments within milliseconds, a probabilistic approach can lead to fatal risks. In industrial settings, even a 99% success rate can be considered a catastrophic failure, making near-perfect reliability paramount for safety. This is because one failure out of 100 operations can lead to worker injury or death, or the loss of millions of dollars worth of equipment. Consequently, the expectation that industrial autom
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