영어 모의고사 (자동 편성)

Hospitals are increasingly adopting artificial intelligence tools that can scan medical images and flag potential tumors faster and, in some trials, more accurately than experienced radiologists. Patients understandably welcome any technology that catches disease earlier, yet the growing reliance on these tools raises an uncomfortable legal question that hospitals ①have only begun to address. When an AI system misses a diagnosis that a human doctor would likely ②have caught, responsibility becomes difficult to assign. The doctor may argue that they simply trusted a tool the hospital ③required them to use, the hospital may argue that the software company guaranteed its accuracy, and the software company may argue that no algorithm can ④expect to be perfect. Until legal systems clarify this chain of responsibility, patients harmed by an algorithmic error may find themselves with no clear party ⑤to hold accountable.

Beyond restoring lost function, engineers are now exploring implants and prosthetics designed to give healthy people abilities beyond ordinary human limits, from night vision to reaction times faster than any athlete could achieve through training alone. Supporters frame this as simply the next step in a long history of human enhancement, ①no different in principle from eyeglasses or vaccines, only more direct. Critics counter that earlier enhancements were broadly available to anyone regardless of wealth, while cutting-edge neural and physical implants are likely to remain ②expensive for decades, potentially splitting society into those who can afford enhancement and those who cannot. If such a divide hardens, competitive advantages in school, sports, and the workplace could come to depend ③less on effort than on access to capital. What began as a medical tool for restoring what was lost, in other words, may end up ④preserving the baseline of what counts as an ordinary human being, keeping the definition of "normal" ⑤unchanged for future generations.

The subscription economy refers to a business model in which consumers pay a recurring fee to access goods or services rather than owning them outright. This model has expanded rapidly beyond entertainment platforms into areas such as clothing, food, and even transportation. One major reason for this growth is ___________, since subscribers no longer need to make repeated purchasing decisions once they sign up. Another reason is that companies benefit from predictable revenue, which allows them to plan production and marketing more efficiently than with one-time sales. However, some critics argue that subscriptions encourage people to pay for services they barely use, quietly draining their monthly budgets.

Factories that once employed hundreds of workers on an assembly line now run with a fraction of that staff, as robotic arms handle tasks that used to require human hands. (A) However, critics point out that earlier transitions unfolded over generations, giving workers time to retrain, whereas today's robots are being deployed much faster. (B) Historical evidence offers some comfort: previous waves of automation eventually created new categories of work that no one had predicted. (C) A factory worker displaced this year, therefore, cannot simply wait for a new industry to emerge decades from now.

As more schools shift toward digital textbooks and online assignments, a student without reliable internet access at home faces a disadvantage that has little to do with talent or effort. (A) That assumption is increasingly being reconsidered, particularly as more essential services, from job applications to medical appointments, move online by default. (B) Some policymakers initially treated internet access as a convenience, comparable to cable television, rather than as infrastructure essential to modern life. (C) Closing this divide will require more than simply distributing devices; it will demand sustained investment in the physical networks that carry information into every home.

Janis identified several warning signs, including an illusion of invulnerability that leads a group to underestimate risk, pressure on dissenters to fall in line, and self-appointed mindguards who shield the group from information that might disrupt its confidence. Even highly capable individuals can make disastrous collective decisions once they are absorbed into a tightly knit group, a phenomenon psychologist Irving Janis termed groupthink. ( ① ) Within cohesive committees, boards, and advisory councils, members often prioritize consensus and group harmony over a rigorous evaluation of alternatives, silencing doubts rather than risking the discomfort of dissent. ( ② ) Sociologists extend this analysis beyond small committees, noting that entire organizations can develop cultures that reward conformity and punish employees who raise inconvenient questions. ( ③ ) Historical inquiries into policy failures and corporate disasters frequently reveal that critical information existed within the organization but never reached decision makers. ( ④ ) This was not because it was hidden, but because no one felt safe enough to voice it aloud. ( ⑤ )

주어진 문장: This flexibility, however, comes with a hidden cost that many workers overlook. Remote work has moved from a rare privilege to a common practice for millions of employees worldwide. ( ① ) Many companies that once required workers to commute daily now allow them to complete tasks from home. ( ② ) Supporters argue that remote work increases productivity, since employees avoid long commutes and distracting office environments. ( ③ ) It also allows companies to hire talented people regardless of where they live, widening the pool of candidates. ( ④ ) Critics, however, worry that remote work weakens teamwork and makes it harder for new employees to learn from experienced colleagues. ( ⑤ ) Without regular face-to-face interaction, some workers feel isolated and struggle to build strong professional relationships.

In other words, no single app may intend to expose such sensitive information, but the combination of many small pieces can. Every time a smartphone connects to a cell tower or a Wi-Fi network, it quietly records a data point that can later be pieced together into a detailed map of where its owner has been. ( ① ) Most users agree to this tracking without reading the fine print, assuming that a single company's database poses little threat. ( ② ) The real danger, however, emerges when location data from multiple sources is combined. ( ③ ) A pattern that reveals a person's home, workplace, and daily route can expose far more than any single app ever intended to share, including medical visits or private relationships. ( ④ ) Companies often argue that anonymized data cannot be traced back to individuals, yet researchers have repeatedly shown otherwise. ( ⑤ )

Until clearer rules emerge, artists, developers, and publishers remain locked in a legal gray zone that satisfies almost no one. Generative AI systems learn to write, draw, and compose by studying enormous collections of existing human work, much of which was created without the artists' knowledge or consent. ( ① ) Supporters of the technology argue that this process resembles how a human student learns by studying the masters. ( ② ) Critics respond that the comparison breaks down at scale, since a single AI model can absorb millions of works in days. ( ③ ) Courts around the world are now being asked to decide whether training an algorithm on copyrighted material counts as fair use or as infringement. ( ④ ) This is a question the law was never designed to answer. ( ⑤ )

In other words, people want the safest outcome for society in theory, but the safest outcome for themselves in practice. When engineers design the software that controls a self-driving car, they inevitably face a question that philosophers have debated for decades. ( ① ) If a crash becomes unavoidable, should the vehicle protect its own passenger at all costs, or should it be programmed to minimize total harm, even if that means steering toward the person inside? ( ② ) Surveys reveal an uncomfortable contradiction: most people say a car should be programmed to save the greater number of lives. ( ③ ) Yet the same people admit they would never buy a vehicle designed to sacrifice its own owner. ( ④ ) This gap between the ideal and the personal choice suggests that ethical programming cannot be settled by public opinion alone. ( ⑤ ) Manufacturers, regulators, and philosophers must instead agree on transparent rules before autonomous vehicles are allowed to make such decisions.