Questões de Concurso

Writing is hard. For Franz Kafka, putting words on the page was a process of “unending torments”. George Orwell said it felt like “a long bout of some painful illness”. Hunter S. Thompson, a journalist, saw it as the opposite of sex: “only good when it’s over”. Now AI has come to the rescue. Large language models (LLMs) eagerly offer reams of text without any pain or torment. Bots can spew out emails (excellently), longer prose (passably) and poetry (poorly). To them, writer’s block is a 500-sheet pack of paper: any user of an LLM can produce as many words in a day as Kafka did in his life.

For human writers this is a blessing and a curse. If you claim to be an author but use AI, you look like a fraud—bot-busting tools like Pangram are good at spotting when someone has had an AI helping hand. […]

If LLMs keep improving, in a few years they could be as good as professional wordsmiths. In the meantime, though, their baggy prose puts a premium on editing skills. After all, even the best writers need a good editor to keep them sharp. Editing is something The Economist deals with day in, day out, so here are our tips for rewriting AI prose.

First, commission well. George R.R. Martin once said the job of editors is to “clip the wings” of writers, and “tell them in which direction to fly”. When it comes to LLMs, that means giving clear instructions about exactly what you want, and asking them to refine their output as many times as necessary. You’ll get less pushback from machines than men. Mr Martin said editors were “the writer’s natural enemy”; AI would probably say “great observation!” and “love the enemy analogy!”

Second, pay attention to detail. AI writers will make mistakes and offer phrases that sound good but mean nothing. They like to lavish their prose with Latinate words, talking about the “bidirectional flow” and how to “expedite grid enhancements”; remind them that Anglo-Saxon words are often punchier. They like clichéd metaphors: there are lots of “Achilles’ heels”. And AI tends to use the “rule of three” far more than humans. The Economist also likes this rhetorical trick. But whereas we try to group three sensible bits of advice, AI offers such redundant sentences as: “The mystery has been solved—not partially, not ambiguously, but definitively.” Such lines should be cut—definitively.

Finally, know your audience. The editor is an intermediary between writer and reader. LLMs are sycophants and their smarmy tone is often off-key. If you are writing an email to break some bad news to your boss, tell your bot to skip the exclamation marks. AI can be pretentious or pontificating (it was trained on human writing, after all). Most people are short of time, but a verbose AI pins you to the wall like a bore at a party.

“Write drunk, edit sober, ” goes the old writers’ maxim. If that’s right, when the AI-human team gets down to work, the AIs will have all the fun.

Adapted from https://www.economist.com/leaders/2026/07/30/ai-is-gettingbetter-at-writing-humans-must-get-better-at-editing

Writing is hard. For Franz Kafka, putting words on the page was a process of “unending torments”. George Orwell said it felt like “a long bout of some painful illness”. Hunter S. Thompson, a journalist, saw it as the opposite of sex: “only good when it’s over”. Now AI has come to the rescue. Large language models (LLMs) eagerly offer reams of text without any pain or torment. Bots can spew out emails (excellently), longer prose (passably) and poetry (poorly). To them, writer’s block is a 500-sheet pack of paper: any user of an LLM can produce as many words in a day as Kafka did in his life.

For human writers this is a blessing and a curse. If you claim to be an author but use AI, you look like a fraud—bot-busting tools like Pangram are good at spotting when someone has had an AI helping hand. […]

If LLMs keep improving, in a few years they could be as good as professional wordsmiths. In the meantime, though, their baggy prose puts a premium on editing skills. After all, even the best writers need a good editor to keep them sharp. Editing is something The Economist deals with day in, day out, so here are our tips for rewriting AI prose.

First, commission well. George R.R. Martin once said the job of editors is to “clip the wings” of writers, and “tell them in which direction to fly”. When it comes to LLMs, that means giving clear instructions about exactly what you want, and asking them to refine their output as many times as necessary. You’ll get less pushback from machines than men. Mr Martin said editors were “the writer’s natural enemy”; AI would probably say “great observation!” and “love the enemy analogy!”

Second, pay attention to detail. AI writers will make mistakes and offer phrases that sound good but mean nothing. They like to lavish their prose with Latinate words, talking about the “bidirectional flow” and how to “expedite grid enhancements”; remind them that Anglo-Saxon words are often punchier. They like clichéd metaphors: there are lots of “Achilles’ heels”. And AI tends to use the “rule of three” far more than humans. The Economist also likes this rhetorical trick. But whereas we try to group three sensible bits of advice, AI offers such redundant sentences as: “The mystery has been solved—not partially, not ambiguously, but definitively.” Such lines should be cut—definitively.

Finally, know your audience. The editor is an intermediary between writer and reader. LLMs are sycophants and their smarmy tone is often off-key. If you are writing an email to break some bad news to your boss, tell your bot to skip the exclamation marks. AI can be pretentious or pontificating (it was trained on human writing, after all). Most people are short of time, but a verbose AI pins you to the wall like a bore at a party.

“Write drunk, edit sober, ” goes the old writers’ maxim. If that’s right, when the AI-human team gets down to work, the AIs will have all the fun.

Adapted from https://www.economist.com/leaders/2026/07/30/ai-is-gettingbetter-at-writing-humans-must-get-better-at-editing

   Over the past couple of years, the growing debate around automated facial recognition has reached a boiling point. As developers have continued to swiftly expand the scope of these kinds of technologies into an almost unbounded range of applications, an increasingly strident chorus of critical voices has sounded concerns about the injurious effects of the proliferation of such systems on impacted individuals and communities. Critics argue that the irresponsible design and use of facial detection and recognition technologies (FDRTs) threaten to violate civil liberties, infringe on basic human rights and further entrench structural racism and systemic marginalisation. In addition, they argue that the gradual creep of face surveillance infrastructures into every domain of lived experience may eventually eradicate the modern democratic forms of life that have long provided cherished means to individual flourishing, social solidarity and human self-creation.

   Defenders, by contrast, emphasise the gains in public safety, security and efficiency that digitally streamlined capacities for facial identification, identity verification and trait characterisation may bring. These proponents point to potential real-world benefits like the added security of facial recognition enhanced border control, the increased efficacy of missing children or criminal suspect searches that are driven by the application of brute force facial analysis to largescale databases and the many added conveniences of facial verification in the business of everyday life.

   Whatever side of the debate on which one lands, it would appear that FDRTs are here to stay.

Adapted from: understanding_bias_in_facial_recognition_technology.pdf