영어 모의고사 (자동 편성)

City governments increasingly market dense networks of surveillance cameras as a straightforward safety upgrade, ①promising faster emergency response and lower crime rates. In many cases, the technology delivers exactly what it promises, ②helping investigators identify suspects within hours rather than weeks. Yet the same infrastructure that catches criminals also creates a permanent record of where ordinary, law-abiding citizens ③go about their daily lives. Once a camera network exists, its purpose can quietly expand, often without new public debate or explicit approval. Civil liberties groups argue that a city should decide how much surveillance it wants before the cameras go up, not years afterward when the infrastructure ④is already impossible removing. Convenience installed today, in other words, can quietly ⑤become control tomorrow.

Imagine two travelers ①preparing for the same trip: one refuses to fly after watching news coverage of a recent plane crash, while the other books a flight without hesitation. Statistically, air travel remains far safer than ②driving the same distance by car, yet vivid, emotionally charged images tend to dominate human judgment more powerfully than dry statistics ever could. This tendency, in which people ③estimate how likely an event is based on how easily examples come to mind rather than on actual data, quietly shapes countless everyday decisions. News broadcasts that repeatedly feature rare disasters can leave viewers ④convinced that such events are common, even though official records clearly show the opposite. Advertisers and politicians alike exploit this pattern, knowing that a single striking anecdote often ⑤persuade audiences more effectively than pages of careful statistics ever manage to.

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

Behavioral economists have long observed that people tend to feel the pain of losing something more intensely than the pleasure of gaining an equivalent amount, a tendency known as loss aversion. Consider an investor who refuses to sell a declining stock, clinging to the hope that its price will eventually recover, even when all available evidence indicates that selling and reinvesting elsewhere would be more rational. As a result, loss aversion frequently pushes individuals toward ___________ rather than toward the outcome that would maximize their overall welfare.

Policymakers seeking to improve public welfare without restricting individual freedom have increasingly embraced an approach known as nudging, which alters the environment in which choices are made rather than mandating particular outcomes. A nudge preserves every option that was previously available; it simply arranges those options so that the choice deemed most beneficial becomes easier or more visible than the alternatives. Despite ongoing debate over its ethics, governments across many countries have adopted nudge-based programs, reporting outcomes that are frequently ___________ compared with those achieved through traditional regulation or public information campaigns alone.

Hospitals are increasingly adopting artificial intelligence tools that can scan medical images and flag potential tumors faster than experienced radiologists. (A) The doctor may argue that they simply trusted a tool the hospital required them to use, while the hospital may blame the software company instead. (B) Yet the growing reliance on these tools raises an uncomfortable legal question: when an AI system misses a diagnosis, who is responsible? (C) Until legal systems clarify this chain of responsibility, patients harmed by an algorithmic error may find themselves with no clear party to hold accountable.

Yet the objective probability of survival in both scenarios is, of course, mathematically identical. Framing effects appear with particular force in decisions involving risk and uncertainty. ( ① ) When physicians describe a treatment option in terms of a survival rate, patients tend to view the treatment favorably and are more inclined to proceed. ( ② ) When the very same statistic is instead presented as a mortality rate, patient enthusiasm for the identical treatment drops noticeably. ( ③ ) This asymmetry persists even among physicians themselves, who might be expected to evaluate probabilities more analytically than the general public. ( ④ ) The persistence of this bias among trained professionals suggests that framing effects operate below the level of deliberate reasoning, influencing judgment before conscious analysis even begins. ( ⑤ ) Understanding this tendency allows communicators to present information more responsibly, regardless of which frame ultimately proves more persuasive.

For this reason, many economists argue that the real challenge is not the robots themselves, but the speed at which society is expected to adapt to them. 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. ( ① ) Economists have long debated whether this shift destroys jobs or simply transforms them. ( ② ) Historical evidence offers some comfort: previous waves of automation eventually created new categories of work that no one had predicted. ( ③ ) However, critics point out that earlier transitions unfolded over generations, giving workers time to retrain, whereas today's robots are being deployed at a pace that leaves little room for adjustment. ( ④ ) A factory worker displaced this year cannot simply wait for a new industry to emerge decades from now. ( ⑤ )

주어진 문장: This vulnerability explains why crucial instructions embedded in the middle of a lengthy list are so often overlooked or misremembered. Anyone designing a presentation, a curriculum, or a grocery list should attend to a well-documented pattern in human memory known as the serial position effect. When people are asked to recall items from a list right after studying it, they consistently remember the items presented at the beginning and end far better than those buried in the middle. ( ① ) The advantage for early items, called the primacy effect, arises because those items receive more rehearsal and are more likely to be transferred into long-term storage before subsequent items compete for attention. ( ② ) The advantage for final items, known as the recency effect, occurs because those items remain accessible in short-term memory at the moment of recall. ( ③ ) Middle items suffer because they must compete for limited rehearsal opportunities while lacking the stability afforded by extended processing. ( ④ ) Communicators who ignore this pattern risk burying their most important point where it will predictably be forgotten. ( ⑤ ) A more effective strategy, therefore, places the crucial message at the beginning or end, precisely where memory proves most reliable.

Several cities have already reported cases in which an innocent person was detained based on a flawed match. Facial recognition software has moved rapidly from airport security lines into shopping malls, schools, and even private apartment buildings, promising convenience in exchange for a quick scan of one's face. ( ① ) Proponents note that the technology can unlock doors, catch shoplifters, and speed up identity checks far faster than any human guard could manage. ( ② ) Independent studies, however, have repeatedly found that many commercial systems perform less accurately on women and people with darker skin tones. ( ③ ) This gap can lead to wrongful identification with serious real-world consequences. ( ④ ) Until these systems can prove equally reliable for everyone, critics argue, deploying them at all represents a form of quiet discrimination dressed up as efficiency. ( ⑤ )