영어 모의고사 (자동 편성)

Sensors buried in the soil now report moisture levels in real time, drones survey entire fields for signs of disease within minutes, and automated systems adjust irrigation without a farmer ever ①walking the rows. For large operations with the capital ②to invest, these tools have measurably increased yields while reducing water waste, addressing two problems that traditional farming has struggled with for generations. Smaller family farms, however, often cannot afford the upfront cost of this equipment, and some worry that the gap between high-tech and traditional operations will only ③widen as the technology advances further. There is also a quieter cultural cost that rarely appears in productivity reports: generations of farming knowledge, passed down through direct observation of soil, weather, and crops, risk ④being replaced by dashboards and automated alerts that few young farmers ⑤teaching to question.

Policymakers seeking to improve public welfare without restricting individual freedom have increasingly embraced an approach ①known as nudging, which alters the environment in which choices are made rather than mandating particular outcomes. A nudge preserves every option that ②was previously available; it simply arranges those options so that the choice deemed most beneficial becomes easier or more visible than the alternatives. Placing fruit at eye level in a school cafeteria while keeping dessert accessible but less prominent, for instance, ③do not forbid any student from choosing dessert, yet it measurably shifts consumption patterns toward healthier food. Advocates argue that such interventions ④respect autonomy precisely because no option is removed and no penalty is imposed for choosing differently. Critics, however, contend that even subtle architectural choices exploit predictable cognitive shortcuts, ⑤raising questions about whether influencing behavior through psychological means is meaningfully different from more coercive forms of persuasion.

Blockchain technology was originally promoted as a way ①to remove the need for trusted middlemen, allowing strangers to exchange value directly and verify transactions through a shared, tamper-resistant ledger rather than through a bank or government agency. In theory, this design ②distributes power away from any single institution and toward the network as a whole. In practice, however, a small number of large players have come to dominate many blockchain networks, ③controlled enough computing power or currency to influence outcomes that were supposed to be decided collectively. Critics note a further irony: when something goes wrong, there is often no institution left ④to appeal to, since the entire system was designed precisely to eliminate that kind of central authority. Whether decentralization can survive this concentration of power ⑤remains one of the technology's most unresolved questions.

Devices that translate brain activity directly into digital commands have moved from laboratory experiments to real medical use, allowing paralyzed patients to control a robotic arm or type a message using thought alone. As the technology ①improves, some developers envision a future in which brain-computer interfaces extend beyond restoring lost function toward enhancing healthy minds. This prospect raises a philosophical question that medicine alone cannot answer: if a device can alter memory, mood, or decision-making, at what point does the assistance stop being a tool and start ②reshaping the very identity of the person using it. Neuroethicists argue that mental privacy, the ability to keep one's own thoughts ③exposed to external access or manipulation, may need to be recognized as a fundamental right before this technology becomes ④widespread. This concern is likely to grow ⑤more urgent as the devices become more powerful.

Traditional industrial systems have relied on a linear model of production, in which raw materials are extracted, transformed into goods, and eventually discarded as waste. This approach treats natural resources as if unlimited and disposal as if costless. A circular economy proposes an alternative framework in which _______________, thereby reducing the extraction of virgin resources. Central to this model is the notion that waste, rather than being an inevitable byproduct, can be redesigned as a resource for another process, much as fallen leaves nourish the trees that shed them. For example, manufacturers design products so that components can be disassembled and reused in new items once their original function ends. Some companies also lease equipment instead of selling it outright, retaining responsibility for refurbishment. Because such practices lessen dependence on finite raw materials, they reduce the environmental damage caused by mining. Consequently, the circular economy is increasingly regarded as a viable economic strategy that conserves resources for future generations.

Many large companies now rely on artificial intelligence to screen job applications before a human ever sees them. These systems promise efficiency, scanning thousands of resumes in seconds and ranking candidates according to patterns drawn from previous hiring decisions. However, this reliance on historical data creates a troubling risk. If a company's past hiring favored a particular group, the algorithm may learn to treat that pattern as a marker of success and quietly reproduce it, even when no one intended discrimination. Unlike a human interviewer, the system offers no explanation for its choices, which means that _______________. Some researchers argue that fairness cannot be added afterward; it must be built into the system's design from the very beginning.

City governments increasingly market dense networks of surveillance cameras as a straightforward safety upgrade, promising faster emergency response and lower crime rates. (A) Yet the same infrastructure that catches criminals also creates a permanent record of where ordinary, law-abiding citizens go about their daily lives. (B) In many cases, the technology delivers exactly what it promises, helping investigators identify suspects within hours rather than weeks. (C) Once a camera network exists, its purpose can quietly expand, from tracking traffic accidents to monitoring political protests, often without new public debate.

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.

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 )