{"id":72796,"date":"2024-05-13T00:05:30","date_gmt":"2024-05-13T04:05:30","guid":{"rendered":"https:\/\/world.edu\/?p=72796"},"modified":"2024-05-13T00:05:30","modified_gmt":"2024-05-13T04:05:30","slug":"ai-assisted-writing-is-quietly-booming-in-academic-journals-heres-why-thats-ok","status":"publish","type":"post","link":"https:\/\/world.edu\/ai-assisted-writing-is-quietly-booming-in-academic-journals-heres-why-thats-ok\/","title":{"rendered":"AI-assisted writing is quietly booming in academic journals. Here\u2019s why that\u2019s OK"},"content":{"rendered":"

\"\"<\/p>\n

If you search Google Scholar for the phrase \u201cas an AI language model<\/a>\u201d, you\u2019ll find plenty of AI research literature and also some rather suspicious results. For example, one\u00a0paper<\/a>\u00a0on agricultural technology says:<\/p>\n

As an AI language model, I don\u2019t have direct access to current research articles or studies. However, I can provide you with an overview of some recent trends and advancements \u2026<\/em><\/p><\/blockquote>\n

Obvious gaffes like this aren\u2019t the only signs that researchers are increasingly turning to generative AI tools when writing up their research. A\u00a0recent study<\/a>\u00a0examined the frequency of certain words in academic writing (such as \u201ccommendable\u201d, \u201cmeticulously\u201d and \u201cintricate\u201d), and found they became far more common after the launch of ChatGPT \u2013 so much so that 1% of all journal articles published in 2023 may have contained AI-generated text.<\/p>\n

(Why do AI models overuse these words? There is\u00a0speculation<\/a>\u00a0it\u2019s because they are more common in English as spoken in Nigeria, where key elements of model training often occur.)<\/p>\n

The aforementioned study also looks at preliminary data from 2024, which indicates that AI writing assistance is only becoming more common. Is this a crisis for modern scholarship, or a boon for academic productivity?<\/p>\n

Who should take credit for AI writing?<\/strong><\/h4>\n

Many people are worried by the use of AI in academic papers. Indeed, the practice has been described as \u201ccontaminating<\/a>\u201d scholarly literature.<\/p>\n

Some argue that using AI output amounts to plagiarism. If your ideas are copy-pasted from ChatGPT, it is questionable whether you really deserve credit for them.<\/p>\n

But there are important differences between \u201cplagiarising\u201d text authored by humans and text authored by AI. Those who plagiarise humans\u2019 work receive credit for ideas that ought to have gone to the original author.<\/p>\n

By contrast, it is debatable whether AI systems like ChatGPT can\u00a0have<\/em>\u00a0ideas, let alone deserve credit for them. An AI tool is\u00a0more like<\/a>\u00a0your phone\u2019s autocomplete function than a human researcher.<\/p>\n

The question of bias<\/strong><\/h4>\n

Another worry is that AI outputs might be biased in ways that could seep into the scholarly record. Infamously, older language models\u00a0tended to portray<\/a>\u00a0people who are female, black and\/or gay in distinctly unflattering ways, compared with people who are male, white and\/or straight.<\/p>\n

This kind of bias is\u00a0less pronounced<\/a>\u00a0in the current version of ChatGPT.<\/p>\n

However, other studies have found a different\u00a0kind<\/a>\u00a0of\u00a0bias<\/a>\u00a0in ChatGPT and\u00a0other large language models<\/a>: a tendency to reflect a left-liberal political ideology.<\/p>\n

Any such bias could subtly distort scholarly writing produced using these tools.<\/p>\n

The hallucination problem<\/strong><\/h4>\n

The most serious worry relates to a well-known limitation of generative AI systems: that they often make serious mistakes.<\/p>\n

These kinds of overconfident mistakes have been referred to as \u201cAI hallucinations<\/a>\u201d and \u201cAI bullshit<\/a>\u201d. While it is easy to spot that the above ASCII image looks nothing like a mushroom (and quite a bit like a snail), it may be much harder to identify any mistakes ChatGPT makes when\u00a0surveying scientific literature<\/a>\u00a0or describing the state of a philosophical debate.<\/p>\n

Unlike (most) humans, AI systems are fundamentally unconcerned with the truth of what they say. If used carelessly, their hallucinations could corrupt the scholarly record.<\/p>\n

Should AI-produced text be banned?<\/strong><\/h4>\n

One response to the rise of text generators has been to ban them outright. For example, Science \u2013 one of the world\u2019s most influential academic journals \u2013 disallows\u00a0any use of AI-generated text<\/a>.<\/p>\n

I see two problems with this approach.<\/p>\n

The first problem is a practical one: current tools for detecting AI-generated text are highly unreliable. This includes the detector created by ChatGPT\u2019s own developers, which was\u00a0taken offline<\/a>\u00a0after it was found to have only a 26% accuracy rate (and a\u00a09% false positive rate<\/a>). Humans also\u00a0make mistakes<\/a>\u00a0when assessing whether something was written by AI.<\/p>\n

It is also possible to circumvent AI text detectors. Online communities are\u00a0actively exploring<\/a>\u00a0how to prompt ChatGPT in ways that allow the user to evade detection. Human users can also superficially rewrite AI outputs, effectively scrubbing away the traces of AI (like its overuse of the words \u201ccommendable\u201d, \u201cmeticulously\u201d and \u201cintricate\u201d).<\/p>\n

The second problem is that banning generative AI outright prevents us from realising these technologies\u2019 benefits. Used well, generative AI can\u00a0boost academic productivity<\/a>\u00a0by streamlining the writing process. In this way, it could help further human knowledge. Ideally, we should try to reap these benefits while avoiding the problems.<\/p>\n

The problem is poor quality control, not AI<\/strong><\/h4>\n

The most serious problem with AI is the risk of introducing unnoticed errors, leading to sloppy scholarship. Instead of banning AI, we should try to ensure that mistaken, implausible or biased claims cannot make it onto the academic record.<\/p>\n

After all, humans can also produce writing with serious errors, and mechanisms such as peer review\u00a0often fail<\/a>\u00a0to prevent its publication.<\/p>\n

We need to get better at ensuring academic papers are free from serious mistakes, regardless of whether these mistakes are caused by careless use of AI or sloppy human scholarship. Not only is this more achievable than policing AI usage, it will improve the standards of academic research as a whole.<\/p>\n

This would be (as ChatGPT might say) a commendable and meticulously intricate solution.<\/p>\n

Author Bio:<\/strong> Julian Koplin is a Lecturer in Bioethics, Monash University & Honorary fellow, Melbourne Law School at Monash University<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

If you search Google Scholar for the phrase \u201cas an AI language model\u201d, you\u2019ll find plenty of AI research literature and also some rather suspicious results. For example, one\u00a0paper\u00a0on agricultural technology says: As an AI language model, I don\u2019t have direct access to current research articles or studies. However, I can provide you with an […]\n… learn more→<\/a><\/p>","protected":false},"author":33,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[434804],"tags":[434933],"coauthors":[11733],"class_list":["post-72796","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence","tag-ai-writing"],"acf":[],"yoast_head":"\nAI-assisted writing is quietly booming in academic journals. 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