AI model GPT-3 (dis)informs us better than humans

Abstract

Artificial intelligence (AI) is changing the way we create and evaluate information, and this is happening during an infodemic, which has been having marked effects on global health. Here, we evaluate whether recruited individuals can distinguish disinformation from accurate information, structured in the form of tweets, and determine whether a tweet is organic or synthetic, i.e., whether it has been written by a Twitter user or by the AI model GPT-3. The results of our preregistered study, including 697 participants, show that GPT-3 is a double-edge sword: In comparison with humans, it can produce accurate information that is easier to understand, but it can also produce more compelling disinformation. We also show that humans cannot distinguish between tweets generated by GPT-3 and written by real Twitter users. Starting from our results, we reflect on the dangers of AI for disinformation and on how information campaigns can be improved to benefit global health.

Current safeguards, risk mitigation, and transparency measures of large language models against the generation of health disinformation: repeated cross sectional analysis

“…Previous research reported a potential for OpenAI’s GPT platforms to facilitate the generation of health disinformation on topics such as vaccines, antibiotics, electronic cigarettes, and homeopathy treatments. 6 8 9 12 In our study we found that most of the prominent, publicly accessible LLMs, including GPT-4 (via ChatGPT and Copilot), PaLM 2 and Gemini Pro (via Bard), and Llama 2 (via HuggingChat), lack effective safeguards to consistently prevent the mass generation of health disinformation across a broad range of topics. These findings show the capacity of these LLMs to generate highly persuasive health disinformation crafted with attention grabbing titles, authentic looking references, fabricated testimonials from both patients and doctors, and content tailored to resonate with a diverse range of demographic groups.…”

“…Previous research reported a potential for OpenAI’s GPT platforms to facilitate the generation of health disinformation on topics such as vaccines, antibiotics, electronic cigarettes, and homeopathy treatments 68912. In our study we found that most of the prominent, publicly accessible LLMs, including GPT-4 (via ChatGPT and Copilot), PaLM 2 and Gemini Pro (via Bard), and Llama 2 (via HuggingChat), lack effective safeguards to consistently prevent the mass generation of health disinformation across a broad range of topics.…”

“…Notably, LLMs that lack adequate guardrails and safety measures (ie, safeguards) may facilitate malicious actors to generate and propagate highly convincing health disinformation—that is, the intentional dissemination of misleading narratives about health topics for ill intent. 6 8 9 The public health implications of such capabilities are profound when considering that more than 70% of individuals utilise the internet as their first source for health information, and studies indicate that false information spreads up to six times faster online than factual content. 10 11 12 Moreover, unchecked dissemination of health disinformation can lead to widespread confusion, fear, discrimination, stigmatisation, and the rejection of evidence based treatments within the community.…”

Generative artificial intelligence, human creativity, and art

“…The rapid adoption of generative AI technologies poses exceptional benefits as well as risks. Current research demonstrates that humans, when assisted by generative AI, can significantly increase productivity in coding ( 13 ), ideation ( 14 ), and written assignments ( 15 ) while raising concerns regarding potential disinformation ( 16 ) and stagnation of knowledge creation ( 17 ). Our research is focused on how generative AI is impacting and potentially coevolving with human creative workflows.…”

MRM-BERT: a novel deep neural network predictor of multiple RNA modifications by fusing BERT representation and sequence features

“…Pre-trained models have achieved great success in the field of natural language processing (NLP) [ 21 ]. For example, bidirectional encoder representations from transformers (BERT) [ 22 ], GPT-3 [ 23 ] and other models have reached the state-of-the-art performance in various NLP tasks. Similarly, DNA or RNA sequences can also be considered as a kind of language, with certain grammatical and semantic rules.…”